diff --git a/notebooks/Block_6/Jupyter Notebook Block 6 - RNN and Image Captioning.ipynb b/notebooks/Block_6/Jupyter Notebook Block 6 - RNN and Image Captioning.ipynb
index de27500dbaf24401a3079b4d58c2e986bfce6cc8..bee73b0425044774c1ffaa37bb27c47e166c7316 100644
--- a/notebooks/Block_6/Jupyter Notebook Block 6 - RNN and Image Captioning.ipynb
+++ b/notebooks/Block_6/Jupyter Notebook Block 6 - RNN and Image Captioning.ipynb
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -40,15 +40,15 @@
],
"source": [
"from __future__ import print_function\n",
- "from keras.callbacks import LambdaCallback\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense\n",
- "from keras.layers import LSTM\n",
- "from keras.models import load_model\n",
- "from keras.optimizers import RMSprop\n",
- "from keras.utils.data_utils import get_file\n",
- "from keras.layers import Bidirectional\n",
- "from keras.layers import Input, Embedding, Dropout, Activation\n",
+ "from tensorflow.keras.callbacks import LambdaCallback\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense\n",
+ "from tensorflow.keras.layers import LSTM\n",
+ "from tensorflow.keras.models import load_model\n",
+ "from tensorflow.keras.optimizers import RMSprop\n",
+ "from tensorflow.keras.utils import get_file\n",
+ "from tensorflow.keras.layers import Bidirectional\n",
+ "from tensorflow.keras.layers import Input, Embedding, Dropout, Activation\n",
"import numpy as np\n",
"import random\n",
"import sys\n",
@@ -99,7 +99,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -128,7 +128,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -177,7 +177,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -227,7 +227,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -248,7 +248,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -301,7 +301,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -351,7 +351,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -373,7 +373,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -409,7 +409,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -456,7 +456,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -468,13 +468,14 @@
" 2602 2 61 33 2601 389 8 483 355 2 44 134 51 564\n",
" 782 7000 21 321 33 8 47 102 14 6998 21 6996 25 21\n",
" 55 2600 1 652 69 22 482 44]\n",
- "[0. 0. 0. ... 0. 0. 0.]\n"
+ "[0. 0. 0. ... 0. 0. 0.]\n",
+ "(33549, 7006)\n"
]
}
],
"source": [
"# separate into input and output\n",
- "from keras.utils import to_categorical\n",
+ "from tensorflow.keras.utils import to_categorical\n",
"sequences = np.array(sequences)\n",
"X, y = sequences[:,:-1], sequences[:,-1]\n",
"y = to_categorical(y, num_classes=vocab_size)\n",
@@ -506,7 +507,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -528,26 +529,26 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Model: \"sequential_1\"\n",
+ "Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
- "embedding_1 (Embedding) (None, 50, 50) 350300 \n",
+ "embedding (Embedding) (None, 50, 50) 350300 \n",
"_________________________________________________________________\n",
- "lstm_1 (LSTM) (None, 50, 100) 60400 \n",
+ "lstm (LSTM) (None, 50, 100) 60400 \n",
"_________________________________________________________________\n",
- "lstm_2 (LSTM) (None, 100) 80400 \n",
+ "lstm_1 (LSTM) (None, 100) 80400 \n",
"_________________________________________________________________\n",
- "dense_1 (Dense) (None, 100) 10100 \n",
+ "dense (Dense) (None, 100) 10100 \n",
"_________________________________________________________________\n",
- "dense_2 (Dense) (None, 7006) 707606 \n",
+ "dense_1 (Dense) (None, 7006) 707606 \n",
"=================================================================\n",
"Total params: 1,208,806\n",
"Trainable params: 1,208,806\n",
@@ -574,452 +575,817 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 22,
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages/tensorflow_core/python/framework/indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
- " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Train on 33549 samples\n",
"Epoch 1/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.7135 - accuracy: 0.8304\n",
- "Epoch 2/200\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages/keras/callbacks/callbacks.py:846: RuntimeWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
- " (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning\n",
- "/Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages/keras/callbacks/callbacks.py:707: RuntimeWarning: Can save best model only with val_loss available, skipping.\n",
- " 'skipping.' % (self.monitor), RuntimeWarning)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.6195 - accuracy: 0.8609\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 7.6015 - accuracy: 0.0256WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 53s 2ms/sample - loss: 7.6019 - accuracy: 0.0256\n",
+ "Epoch 2/200\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 7.1517 - accuracy: 0.0273WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 7.1512 - accuracy: 0.0273\n",
"Epoch 3/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.5955 - accuracy: 0.8678\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 7.0093 - accuracy: 0.0265WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 7.0095 - accuracy: 0.0265\n",
"Epoch 4/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.5874 - accuracy: 0.8691\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 6.8323 - accuracy: 0.0302WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 6.8321 - accuracy: 0.0302\n",
"Epoch 5/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.5757 - accuracy: 0.8713\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 6.6149 - accuracy: 0.0361WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 6.6146 - accuracy: 0.0361\n",
"Epoch 6/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.5658 - accuracy: 0.8734\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 6.4739 - accuracy: 0.0386WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 6.4739 - accuracy: 0.0386\n",
"Epoch 7/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.5499 - accuracy: 0.8765\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 6.3614 - accuracy: 0.0415WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 6.3614 - accuracy: 0.0416\n",
"Epoch 8/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.5176 - accuracy: 0.8850\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 6.2550 - accuracy: 0.0440WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 6.2547 - accuracy: 0.0439\n",
"Epoch 9/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.5043 - accuracy: 0.8905\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 6.1668 - accuracy: 0.0447WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 6.1670 - accuracy: 0.0447\n",
"Epoch 10/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.4991 - accuracy: 0.8892\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 6.0883 - accuracy: 0.0478WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 6.0882 - accuracy: 0.0478\n",
"Epoch 11/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.5066 - accuracy: 0.8873\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 6.0216 - accuracy: 0.0502WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 6.0221 - accuracy: 0.0502\n",
"Epoch 12/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.4844 - accuracy: 0.8918\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.9525 - accuracy: 0.0515WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.9524 - accuracy: 0.0515\n",
"Epoch 13/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.4792 - accuracy: 0.8944\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.8864 - accuracy: 0.0522WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.8869 - accuracy: 0.0522\n",
"Epoch 14/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.4472 - accuracy: 0.9035\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.8199 - accuracy: 0.0543WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 49s 1ms/sample - loss: 5.8197 - accuracy: 0.0544\n",
"Epoch 15/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.4332 - accuracy: 0.9072\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.7495 - accuracy: 0.0550WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.7498 - accuracy: 0.0550\n",
"Epoch 16/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.4131 - accuracy: 0.9137\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.6753 - accuracy: 0.0578WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.6755 - accuracy: 0.0578\n",
"Epoch 17/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.4319 - accuracy: 0.9047\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.5986 - accuracy: 0.0590WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 49s 1ms/sample - loss: 5.5989 - accuracy: 0.0590\n",
"Epoch 18/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.4417 - accuracy: 0.9026\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.5267 - accuracy: 0.0613WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.5264 - accuracy: 0.0613\n",
"Epoch 19/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.3959 - accuracy: 0.9141\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.4496 - accuracy: 0.0628WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.4498 - accuracy: 0.0628\n",
"Epoch 20/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3695 - accuracy: 0.9230\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.3814 - accuracy: 0.0650WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.3812 - accuracy: 0.0650\n",
"Epoch 21/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.3545 - accuracy: 0.9271\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.3068 - accuracy: 0.0672WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 49s 1ms/sample - loss: 5.3069 - accuracy: 0.0672\n",
"Epoch 22/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.3456 - accuracy: 0.9286\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.2312 - accuracy: 0.0700WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 49s 1ms/sample - loss: 5.2310 - accuracy: 0.0700\n",
"Epoch 23/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3454 - accuracy: 0.9283\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.1572 - accuracy: 0.0718WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.1572 - accuracy: 0.0718\n",
"Epoch 24/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3284 - accuracy: 0.9329\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.0897 - accuracy: 0.0745WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 49s 1ms/sample - loss: 5.0898 - accuracy: 0.0745\n",
"Epoch 25/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.3292 - accuracy: 0.9334\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 5.0156 - accuracy: 0.0769WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 5.0158 - accuracy: 0.0769\n",
"Epoch 26/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.3389 - accuracy: 0.9274\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.9431 - accuracy: 0.0803WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.9432 - accuracy: 0.0802\n",
"Epoch 27/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3371 - accuracy: 0.9285\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.8671 - accuracy: 0.0843WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.8673 - accuracy: 0.0843\n",
"Epoch 28/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3288 - accuracy: 0.9307\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.7983 - accuracy: 0.0873WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.7986 - accuracy: 0.0873\n",
"Epoch 29/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3596 - accuracy: 0.9199\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.7307 - accuracy: 0.0912WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 4.7307 - accuracy: 0.0912\n",
"Epoch 30/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.3544 - accuracy: 0.9206\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.6548 - accuracy: 0.0961WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 4.6546 - accuracy: 0.0962\n",
"Epoch 31/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3057 - accuracy: 0.9366\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.5846 - accuracy: 0.1013WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.5846 - accuracy: 0.1013\n",
"Epoch 32/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2730 - accuracy: 0.9444\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.5059 - accuracy: 0.1088WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 4.5060 - accuracy: 0.1088\n",
"Epoch 33/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2526 - accuracy: 0.9517\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.4415 - accuracy: 0.1146WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.4416 - accuracy: 0.1146\n",
"Epoch 34/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2453 - accuracy: 0.9530\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.3711 - accuracy: 0.1222WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.3711 - accuracy: 0.1222\n",
"Epoch 35/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2253 - accuracy: 0.9589\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.3028 - accuracy: 0.1302WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.3029 - accuracy: 0.1302\n",
"Epoch 36/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2199 - accuracy: 0.9595\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.2402 - accuracy: 0.1380WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 4.2402 - accuracy: 0.1379\n",
"Epoch 37/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2138 - accuracy: 0.9605\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.1797 - accuracy: 0.1457WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 4.1796 - accuracy: 0.1457\n",
"Epoch 38/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2390 - accuracy: 0.9534\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.1106 - accuracy: 0.1569WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.1106 - accuracy: 0.1570\n",
"Epoch 39/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2317 - accuracy: 0.9548\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 4.0483 - accuracy: 0.1647WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 4.0485 - accuracy: 0.1647\n",
"Epoch 40/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2366 - accuracy: 0.9532\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.9894 - accuracy: 0.1724WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.9895 - accuracy: 0.1724\n",
"Epoch 41/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3469 - accuracy: 0.9136\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.9309 - accuracy: 0.1811WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.9309 - accuracy: 0.1811\n",
"Epoch 42/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3069 - accuracy: 0.9276\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.8759 - accuracy: 0.1899WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.8759 - accuracy: 0.1899\n",
"Epoch 43/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2429 - accuracy: 0.9502\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.8169 - accuracy: 0.1981WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.8172 - accuracy: 0.1980\n",
"Epoch 44/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2410 - accuracy: 0.9507\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.7583 - accuracy: 0.2088WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.7582 - accuracy: 0.2088\n",
"Epoch 45/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2035 - accuracy: 0.9611\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.7058 - accuracy: 0.2167WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.7060 - accuracy: 0.2167\n",
"Epoch 46/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1640 - accuracy: 0.9721\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.6524 - accuracy: 0.2301WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.6523 - accuracy: 0.2301\n",
"Epoch 47/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1474 - accuracy: 0.9772\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.6044 - accuracy: 0.2360WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.6045 - accuracy: 0.2360\n",
"Epoch 48/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1361 - accuracy: 0.9795\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.5476 - accuracy: 0.2443WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.5478 - accuracy: 0.2443\n",
"Epoch 49/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1292 - accuracy: 0.9818\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.5011 - accuracy: 0.2523WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.5014 - accuracy: 0.2523\n",
"Epoch 50/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.1345 - accuracy: 0.9796\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.4601 - accuracy: 0.2603WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.4600 - accuracy: 0.2602\n",
"Epoch 51/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1297 - accuracy: 0.9804\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.4058 - accuracy: 0.2702WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.4059 - accuracy: 0.2702\n",
"Epoch 52/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1362 - accuracy: 0.9796\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.3570 - accuracy: 0.2793WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.3572 - accuracy: 0.2793\n",
"Epoch 53/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1405 - accuracy: 0.9779\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.3133 - accuracy: 0.2850WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.3131 - accuracy: 0.2850\n",
"Epoch 54/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1707 - accuracy: 0.9673\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.2754 - accuracy: 0.2924WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.2753 - accuracy: 0.2924\n",
"Epoch 55/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2617 - accuracy: 0.9374\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.2363 - accuracy: 0.3007WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.2361 - accuracy: 0.3008\n",
"Epoch 56/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.4248 - accuracy: 0.8834\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.1929 - accuracy: 0.3083WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.1927 - accuracy: 0.3084\n",
"Epoch 57/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2952 - accuracy: 0.9257\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.1515 - accuracy: 0.3143WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.1519 - accuracy: 0.3143\n",
"Epoch 58/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1748 - accuracy: 0.9654\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.1186 - accuracy: 0.3206WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.1188 - accuracy: 0.3206\n",
"Epoch 59/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.1286 - accuracy: 0.9807\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.0711 - accuracy: 0.3320WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.0712 - accuracy: 0.3320\n",
"Epoch 60/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.1010 - accuracy: 0.9866\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.0397 - accuracy: 0.3364WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 3.0399 - accuracy: 0.3364\n",
"Epoch 61/200\n",
- "33549/33549 [==============================] - 54s 2ms/step - loss: 0.0850 - accuracy: 0.9901\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 3.0060 - accuracy: 0.3419WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 3.0060 - accuracy: 0.3419\n",
"Epoch 62/200\n",
- "33549/33549 [==============================] - 56s 2ms/step - loss: 0.0754 - accuracy: 0.9923\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.9942 - accuracy: 0.3403WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.9945 - accuracy: 0.3402\n",
"Epoch 63/200\n",
- "33549/33549 [==============================] - 56s 2ms/step - loss: 0.0702 - accuracy: 0.9932\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.9397 - accuracy: 0.3525WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.9392 - accuracy: 0.3526\n",
"Epoch 64/200\n",
- "33549/33549 [==============================] - 55s 2ms/step - loss: 0.0688 - accuracy: 0.9933\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.8967 - accuracy: 0.3592WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.8966 - accuracy: 0.3592\n",
"Epoch 65/200\n",
- "33549/33549 [==============================] - 55s 2ms/step - loss: 0.0708 - accuracy: 0.9928\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.8706 - accuracy: 0.3664WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.8707 - accuracy: 0.3663\n",
"Epoch 66/200\n",
- "33549/33549 [==============================] - 54s 2ms/step - loss: 0.0682 - accuracy: 0.9934\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.8385 - accuracy: 0.3706WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.8387 - accuracy: 0.3705\n",
"Epoch 67/200\n",
- "33549/33549 [==============================] - 55s 2ms/step - loss: 0.0700 - accuracy: 0.9923\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.8082 - accuracy: 0.3757WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.8081 - accuracy: 0.3757\n",
"Epoch 68/200\n",
- "33549/33549 [==============================] - 54s 2ms/step - loss: 0.0757 - accuracy: 0.9917\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.7849 - accuracy: 0.3771WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.7850 - accuracy: 0.3771\n",
"Epoch 69/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.0905 - accuracy: 0.9875\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.7496 - accuracy: 0.3854WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.7495 - accuracy: 0.3854\n",
"Epoch 70/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.2912 - accuracy: 0.9215\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.7183 - accuracy: 0.3910WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.7185 - accuracy: 0.3910\n",
"Epoch 71/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.4562 - accuracy: 0.8694\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.6926 - accuracy: 0.3980WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.6927 - accuracy: 0.3980\n",
"Epoch 72/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.4100 - accuracy: 0.8840\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.6641 - accuracy: 0.4029WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.6643 - accuracy: 0.4028\n",
"Epoch 73/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.2152 - accuracy: 0.9476\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.6417 - accuracy: 0.4046WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.6418 - accuracy: 0.4046\n",
"Epoch 74/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.1035 - accuracy: 0.9845\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.6221 - accuracy: 0.4098WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.6222 - accuracy: 0.4098\n",
"Epoch 75/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.0768 - accuracy: 0.9910\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.5986 - accuracy: 0.4140WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.5987 - accuracy: 0.4141\n",
"Epoch 76/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.0561 - accuracy: 0.9957\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.5569 - accuracy: 0.4222WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.5570 - accuracy: 0.4221\n",
"Epoch 77/200\n",
- "33549/33549 [==============================] - 52s 2ms/step - loss: 0.0489 - accuracy: 0.9963\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.5347 - accuracy: 0.4278WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.5349 - accuracy: 0.4278\n",
"Epoch 78/200\n",
- "33549/33549 [==============================] - 53s 2ms/step - loss: 0.0445 - accuracy: 0.9971\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.5190 - accuracy: 0.4299WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.5192 - accuracy: 0.4299\n",
"Epoch 79/200\n",
- "33549/33549 [==============================] - 51s 2ms/step - loss: 0.0408 - accuracy: 0.9973\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.5160 - accuracy: 0.4271WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.5157 - accuracy: 0.4272\n",
"Epoch 80/200\n",
- "33549/33549 [==============================] - 55s 2ms/step - loss: 0.0385 - accuracy: 0.9976\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.4712 - accuracy: 0.4372WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.4711 - accuracy: 0.4372\n",
"Epoch 81/200\n",
- "33549/33549 [==============================] - 53s 2ms/step - loss: 0.0384 - accuracy: 0.9973\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.4473 - accuracy: 0.4414WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 2.4472 - accuracy: 0.4414\n",
"Epoch 82/200\n",
- "33549/33549 [==============================] - 50s 1ms/step - loss: 0.0370 - accuracy: 0.9977\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.4155 - accuracy: 0.4514WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.4154 - accuracy: 0.4515\n",
"Epoch 83/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.0339 - accuracy: 0.9981\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.3977 - accuracy: 0.4535WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.3975 - accuracy: 0.4535\n",
"Epoch 84/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0327 - accuracy: 0.9985\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.3798 - accuracy: 0.4548WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.3797 - accuracy: 0.4549\n",
"Epoch 85/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0306 - accuracy: 0.9986\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.3571 - accuracy: 0.4598WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.3570 - accuracy: 0.4598\n",
"Epoch 86/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0452 - accuracy: 0.9964\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.3404 - accuracy: 0.4629WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.3407 - accuracy: 0.4628\n",
"Epoch 87/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0950 - accuracy: 0.9817\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.3224 - accuracy: 0.4678WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.3225 - accuracy: 0.4678\n",
"Epoch 88/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.3596 - accuracy: 0.8983\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.3009 - accuracy: 0.4702WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.3010 - accuracy: 0.4701\n",
"Epoch 89/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.4860 - accuracy: 0.8594\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.2780 - accuracy: 0.4740WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.2782 - accuracy: 0.4739\n",
"Epoch 90/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.3268 - accuracy: 0.9072\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.2663 - accuracy: 0.4786WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.2663 - accuracy: 0.4786\n",
"Epoch 91/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.1272 - accuracy: 0.9731\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.2450 - accuracy: 0.4796WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.2452 - accuracy: 0.4796\n",
"Epoch 92/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.0640 - accuracy: 0.9926\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.2341 - accuracy: 0.4837WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.2344 - accuracy: 0.4837\n",
"Epoch 93/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0427 - accuracy: 0.9969\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.2122 - accuracy: 0.4875WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.2124 - accuracy: 0.4874\n",
"Epoch 94/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0345 - accuracy: 0.9981\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.1793 - accuracy: 0.4960WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.1793 - accuracy: 0.4960\n",
"Epoch 95/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0289 - accuracy: 0.9990\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.1565 - accuracy: 0.5007WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.1566 - accuracy: 0.5006\n",
"Epoch 96/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0270 - accuracy: 0.9990\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.1420 - accuracy: 0.5010WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.1420 - accuracy: 0.5010\n",
"Epoch 97/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0245 - accuracy: 0.9992\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.1189 - accuracy: 0.5081WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.1189 - accuracy: 0.5081\n",
"Epoch 98/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0236 - accuracy: 0.9992\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.1183 - accuracy: 0.5060WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.1186 - accuracy: 0.5060\n",
"Epoch 99/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0220 - accuracy: 0.9994\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.0925 - accuracy: 0.5143WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 2.0924 - accuracy: 0.5143\n",
"Epoch 100/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0211 - accuracy: 0.9994\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.0765 - accuracy: 0.5176WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.0764 - accuracy: 0.5177\n",
"Epoch 101/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0209 - accuracy: 0.9995\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.0518 - accuracy: 0.5211WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.0518 - accuracy: 0.5211\n",
"Epoch 102/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0186 - accuracy: 0.9997\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.0427 - accuracy: 0.5235WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.0425 - accuracy: 0.5235\n",
"Epoch 103/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0170 - accuracy: 0.9998\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 2.0103 - accuracy: 0.5310WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 2.0104 - accuracy: 0.5309\n",
"Epoch 104/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0161 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.9949 - accuracy: 0.5353WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.9947 - accuracy: 0.5354\n",
"Epoch 105/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0172 - accuracy: 0.9996\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.9812 - accuracy: 0.5350WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.9813 - accuracy: 0.5350\n",
"Epoch 106/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0154 - accuracy: 0.9998\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.9673 - accuracy: 0.5397WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.9677 - accuracy: 0.5396\n",
"Epoch 107/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0145 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.9609 - accuracy: 0.5386WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.9611 - accuracy: 0.5386\n",
"Epoch 108/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0154 - accuracy: 0.9996\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.9310 - accuracy: 0.5461WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.9313 - accuracy: 0.5460\n",
"Epoch 109/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0222 - accuracy: 0.9986\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.9283 - accuracy: 0.5480WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.9284 - accuracy: 0.5479\n",
"Epoch 110/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.5019 - accuracy: 0.8702\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.8997 - accuracy: 0.5525WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.8997 - accuracy: 0.5525\n",
"Epoch 111/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.7531 - accuracy: 0.7929\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.8885 - accuracy: 0.5559WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.8887 - accuracy: 0.5559\n",
"Epoch 112/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.2892 - accuracy: 0.9152\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.8712 - accuracy: 0.5583WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.8712 - accuracy: 0.5583\n",
"Epoch 113/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.1286 - accuracy: 0.9689\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.8687 - accuracy: 0.5610WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.8686 - accuracy: 0.5611\n",
"Epoch 114/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0482 - accuracy: 0.9955\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.8495 - accuracy: 0.5645WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.8495 - accuracy: 0.5645\n",
"Epoch 115/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.0292 - accuracy: 0.9987\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.8277 - accuracy: 0.5694WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.8275 - accuracy: 0.5695\n",
"Epoch 116/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.0224 - accuracy: 0.9995\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.8087 - accuracy: 0.5715WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.8084 - accuracy: 0.5716\n",
"Epoch 117/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0194 - accuracy: 0.9997\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.7855 - accuracy: 0.5787WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.7852 - accuracy: 0.5788\n",
"Epoch 118/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0180 - accuracy: 0.9998\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.7622 - accuracy: 0.5824WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.7620 - accuracy: 0.5823\n",
"Epoch 119/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0165 - accuracy: 0.9998\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.7621 - accuracy: 0.5810WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.7619 - accuracy: 0.5810\n",
"Epoch 120/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0151 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.7591 - accuracy: 0.5838WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.7592 - accuracy: 0.5837\n",
"Epoch 121/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0140 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.7241 - accuracy: 0.5911WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.7242 - accuracy: 0.5910\n",
"Epoch 122/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0132 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.7056 - accuracy: 0.5967WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.7059 - accuracy: 0.5966\n",
"Epoch 123/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0127 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.7064 - accuracy: 0.5963WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.7064 - accuracy: 0.5963\n",
"Epoch 124/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0118 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.6958 - accuracy: 0.5958WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.6960 - accuracy: 0.5957\n",
"Epoch 125/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.0112 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.6797 - accuracy: 0.6000WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.6800 - accuracy: 0.6000\n",
"Epoch 126/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0106 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.6657 - accuracy: 0.6036WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.6660 - accuracy: 0.6036\n",
"Epoch 127/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0102 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.6428 - accuracy: 0.6090WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.6426 - accuracy: 0.6091\n",
"Epoch 128/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0096 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.6215 - accuracy: 0.6142WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.6215 - accuracy: 0.6142\n",
"Epoch 129/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0093 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.6349 - accuracy: 0.6091WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.6348 - accuracy: 0.6091\n",
"Epoch 130/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.0090 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.6177 - accuracy: 0.6121WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.6178 - accuracy: 0.6120\n",
"Epoch 131/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0097 - accuracy: 0.9998\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.5900 - accuracy: 0.6198WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.5900 - accuracy: 0.6198\n",
"Epoch 132/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0088 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.5646 - accuracy: 0.6270WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.5643 - accuracy: 0.6271\n",
"Epoch 133/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0105 - accuracy: 0.9997\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.5621 - accuracy: 0.6268WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.5622 - accuracy: 0.6268\n",
"Epoch 134/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.2695 - accuracy: 0.9273\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.5661 - accuracy: 0.6259WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.5663 - accuracy: 0.6258\n",
"Epoch 135/200\n",
- "33549/33549 [==============================] - 49s 1ms/step - loss: 0.8885 - accuracy: 0.7680\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.5490 - accuracy: 0.6277WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.5489 - accuracy: 0.6277\n",
"Epoch 136/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3778 - accuracy: 0.8903\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.5199 - accuracy: 0.6350WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.5198 - accuracy: 0.6350\n",
"Epoch 137/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.1329 - accuracy: 0.9665\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.5261 - accuracy: 0.6338WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.5261 - accuracy: 0.6338\n",
"Epoch 138/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0520 - accuracy: 0.9933\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.4908 - accuracy: 0.6413WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.4905 - accuracy: 0.6414\n",
"Epoch 139/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0253 - accuracy: 0.9990\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.4639 - accuracy: 0.6512WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.4637 - accuracy: 0.6513\n",
"Epoch 140/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0184 - accuracy: 0.9998\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.4618 - accuracy: 0.6485WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.4618 - accuracy: 0.6485\n",
"Epoch 141/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0152 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.4472 - accuracy: 0.6539WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.4471 - accuracy: 0.6539\n",
"Epoch 142/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0135 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.4373 - accuracy: 0.6557WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.4372 - accuracy: 0.6557\n",
"Epoch 143/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0124 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.4504 - accuracy: 0.6492WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.4505 - accuracy: 0.6492\n",
"Epoch 144/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0121 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.4328 - accuracy: 0.6540WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.4330 - accuracy: 0.6539\n",
"Epoch 145/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0107 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.4204 - accuracy: 0.6559WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.4203 - accuracy: 0.6559\n",
"Epoch 146/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0099 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3985 - accuracy: 0.6629WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.3986 - accuracy: 0.6629\n",
"Epoch 147/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0100 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3763 - accuracy: 0.6692WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.3763 - accuracy: 0.6692\n",
"Epoch 148/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0090 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3575 - accuracy: 0.6757WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.3575 - accuracy: 0.6757\n",
"Epoch 149/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0084 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3612 - accuracy: 0.6738WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.3615 - accuracy: 0.6737\n",
"Epoch 150/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0080 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3751 - accuracy: 0.6689WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.3752 - accuracy: 0.6689\n",
"Epoch 151/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0075 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3438 - accuracy: 0.6749WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.3441 - accuracy: 0.6747\n",
"Epoch 152/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0073 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3398 - accuracy: 0.6752WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.3397 - accuracy: 0.6752\n",
"Epoch 153/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0069 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3187 - accuracy: 0.6811WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.3185 - accuracy: 0.6812\n",
"Epoch 154/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0066 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.3064 - accuracy: 0.6835WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.3065 - accuracy: 0.6835\n",
"Epoch 155/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0064 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2743 - accuracy: 0.6935WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 49s 1ms/sample - loss: 1.2742 - accuracy: 0.6935\n",
"Epoch 156/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0060 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2659 - accuracy: 0.6971WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.2661 - accuracy: 0.6971\n",
"Epoch 157/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0058 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2636 - accuracy: 0.6966WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.2634 - accuracy: 0.6966\n",
"Epoch 158/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0055 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2448 - accuracy: 0.6991WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.2450 - accuracy: 0.6991\n",
"Epoch 159/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0053 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2827 - accuracy: 0.6882WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.2828 - accuracy: 0.6882\n",
"Epoch 160/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0075 - accuracy: 0.9997\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2369 - accuracy: 0.6989WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.2370 - accuracy: 0.6989\n",
"Epoch 161/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3042 - accuracy: 0.9203\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2147 - accuracy: 0.7076WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.2145 - accuracy: 0.7077\n",
"Epoch 162/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.9160 - accuracy: 0.7669\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2147 - accuracy: 0.7052WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.2147 - accuracy: 0.7052\n",
"Epoch 163/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.3752 - accuracy: 0.8936\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.2157 - accuracy: 0.7043WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.2156 - accuracy: 0.7043\n",
"Epoch 164/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.1138 - accuracy: 0.9706\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.1859 - accuracy: 0.7139WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 1.1859 - accuracy: 0.7138\n",
"Epoch 165/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0397 - accuracy: 0.9957\n",
- "Epoch 166/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0196 - accuracy: 0.9995\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.1531 - accuracy: 0.7207WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.1531 - accuracy: 0.7208\n",
"Epoch 167/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0136 - accuracy: 1.0000\n",
- "Epoch 168/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0123 - accuracy: 0.9999\n",
- "Epoch 169/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0109 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.1415 - accuracy: 0.7232WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 1.1414 - accuracy: 0.7232\n",
"Epoch 170/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0096 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.1168 - accuracy: 0.7312WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 1.1166 - accuracy: 0.7312\n",
"Epoch 171/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0089 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.1078 - accuracy: 0.7320WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 1.1080 - accuracy: 0.7319\n",
"Epoch 172/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0085 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.1192 - accuracy: 0.7290WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 1.1190 - accuracy: 0.7291\n",
"Epoch 173/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0078 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.1254 - accuracy: 0.7261WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.1256 - accuracy: 0.7260\n",
"Epoch 174/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0073 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.1307 - accuracy: 0.7236WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.1307 - accuracy: 0.7236\n",
"Epoch 175/200\n",
- "33549/33549 [==============================] - 48s 1ms/step - loss: 0.0068 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0979 - accuracy: 0.7309WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.0978 - accuracy: 0.7309\n",
"Epoch 176/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0065 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0695 - accuracy: 0.7407WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.0695 - accuracy: 0.7407\n",
"Epoch 177/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0062 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0534 - accuracy: 0.7440WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.0534 - accuracy: 0.7440\n",
"Epoch 178/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0059 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0387 - accuracy: 0.7470WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.0389 - accuracy: 0.7470\n",
"Epoch 179/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0056 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0216 - accuracy: 0.7530WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.0216 - accuracy: 0.7529\n",
"Epoch 180/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0053 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0215 - accuracy: 0.7544WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 48s 1ms/sample - loss: 1.0214 - accuracy: 0.7544\n",
"Epoch 181/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0052 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0024 - accuracy: 0.7589WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.0024 - accuracy: 0.7589\n",
"Epoch 182/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0049 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0034 - accuracy: 0.7576WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.0032 - accuracy: 0.7577\n",
"Epoch 183/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0047 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9853 - accuracy: 0.7616WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9855 - accuracy: 0.7615\n",
"Epoch 184/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0045 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9828 - accuracy: 0.7642WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9830 - accuracy: 0.7641\n",
"Epoch 185/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0043 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9833 - accuracy: 0.7598WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9837 - accuracy: 0.7597\n",
"Epoch 186/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0042 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9894 - accuracy: 0.7581WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9898 - accuracy: 0.7580\n",
"Epoch 187/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0040 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 1.0017 - accuracy: 0.7554WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 1.0016 - accuracy: 0.7554\n",
"Epoch 188/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0038 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9590 - accuracy: 0.7679WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9590 - accuracy: 0.7679\n",
"Epoch 189/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0042 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9440 - accuracy: 0.7728WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9441 - accuracy: 0.7728\n",
"Epoch 190/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.4229 - accuracy: 0.8956\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9322 - accuracy: 0.7750WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9323 - accuracy: 0.7750\n",
"Epoch 191/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.7835 - accuracy: 0.7963\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9246 - accuracy: 0.7771WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9244 - accuracy: 0.7772\n",
"Epoch 192/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.2470 - accuracy: 0.9272\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9264 - accuracy: 0.7781WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9264 - accuracy: 0.7781\n",
"Epoch 193/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0818 - accuracy: 0.9813\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9295 - accuracy: 0.7738WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 46s 1ms/sample - loss: 0.9294 - accuracy: 0.7738\n",
"Epoch 194/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0298 - accuracy: 0.9969\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.9172 - accuracy: 0.7759WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.9172 - accuracy: 0.7760\n",
"Epoch 195/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0153 - accuracy: 0.9996\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.8771 - accuracy: 0.7895WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.8771 - accuracy: 0.7895\n",
"Epoch 196/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0105 - accuracy: 0.9999\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.8834 - accuracy: 0.7864WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.8836 - accuracy: 0.7864\n",
"Epoch 197/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0088 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.8764 - accuracy: 0.7882WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.8765 - accuracy: 0.7882\n",
"Epoch 198/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0078 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.8671 - accuracy: 0.7904WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.8670 - accuracy: 0.7904\n",
"Epoch 199/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0071 - accuracy: 1.0000\n",
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.8518 - accuracy: 0.7943WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.8519 - accuracy: 0.7943\n",
"Epoch 200/200\n",
- "33549/33549 [==============================] - 47s 1ms/step - loss: 0.0066 - accuracy: 1.0000\n"
+ "33536/33549 [============================>.] - ETA: 0s - loss: 0.8349 - accuracy: 0.8001WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
+ "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n",
+ "33549/33549 [==============================] - 47s 1ms/sample - loss: 0.8348 - accuracy: 0.8001\n"
]
},
{
"data": {
"text/plain": [
- "<keras.callbacks.callbacks.History at 0x145b5bfd0>"
+ "<tensorflow.python.keras.callbacks.History at 0x7fe36a0318d0>"
]
},
- "execution_count": 16,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "from keras import callbacks\n",
+ "from tensorflow.keras import callbacks\n",
"model_checkpoint =callbacks.ModelCheckpoint(\"my_checkpoint.h5\", save_best_only=True)\n",
"early_stopping = callbacks.EarlyStopping(patience=50)\n",
"# compile model\n",
@@ -1043,7 +1409,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -1078,7 +1444,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -1111,7 +1477,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -1140,15 +1506,20 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages/tensorflow_core/python/framework/indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
- " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
+ "ename": "AttributeError",
+ "evalue": "'str' object has no attribute 'decode'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-26-51509c3e7321>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# load the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model_goethe_generator.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m 144\u001b[0m if (h5py is not None and (\n\u001b[1;32m 145\u001b[0m isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):\n\u001b[0;32m--> 146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mhdf5_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model_from_hdf5\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py\u001b[0m in \u001b[0;36mload_model_from_hdf5\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodel_config\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No model found in config file.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m \u001b[0mmodel_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 167\u001b[0m model = model_config_lib.model_from_config(model_config,\n\u001b[1;32m 168\u001b[0m custom_objects=custom_objects)\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'decode'"
]
}
],
@@ -1166,7 +1537,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
@@ -1187,14 +1558,14 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "garten margarete an faustens arm marthe mit mephistopheles auf und ab spazirend margarete ich fühl es wohl daß mich der herr nur schont herab sich läßt mich zu beschämen ein reisender ist so gewohnt aus gütigkeit fürlieb zu nehmen ich weiß zu gut daß solch erfahrnen mann mein arm gespräch nicht\n",
+ "ich fühls du schwebst um mich erflehter geist enthülle dich ha wies in meinem herzen reißt zu neuen gefühlen all meine sinnen sich erwühlen ich fühle ganz mein herz dir hingegeben du mußt du mußt und kostet es mein leben er faßt das buch und spricht das zeichen des geistes geheimnißvoll\n",
"\n"
]
}
@@ -1218,7 +1589,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -1226,10 +1597,10 @@
"output_type": "stream",
"text": [
"(1, 50)\n",
- "[[ 769 50 31 5274 417 99 17 15 26 1 159 5275 50 2\n",
- " 355 13 61 40 22 4 110 28 5276 684 19 191 22 8\n",
- " 5277 6 1559 10 14 1415 55 5278 5279 8 622 2 148 8\n",
- " 102 40 664 5280 108 44 417 1922]]\n"
+ "[[ 2 1813 11 3053 69 22 3054 83 3055 36 848 696 9 128\n",
+ " 169 3056 8 343 3057 174 105 341 19 3058 2 607 140 44\n",
+ " 134 45 1814 11 255 11 255 1 3059 13 44 115 24 264\n",
+ " 7 370 1 817 7 454 49 844]]\n"
]
}
],
@@ -1250,14 +1621,14 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[5]\n"
+ "[1309]\n"
]
}
],
@@ -1276,14 +1647,14 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "nicht\n"
+ "geheimnißvoll\n"
]
}
],
@@ -1307,11 +1678,11 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
- "from keras.preprocessing.sequence import pad_sequences\n",
+ "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"encoded = pad_sequences([encoded], maxlen=seq_length, truncating='pre')"
]
},
@@ -1324,7 +1695,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
@@ -1361,14 +1732,14 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "unterhalten kann faust ein blick von dir ein wort mehr unterhält als alle weisheit dieser welt er küßt ihre hand margarete incommodirt euch nicht wie könnt ihr sie nur küssen sie ist so garstig ist so rauh was hab ich nicht schon alles schaffen müssen die mutter ist gar zu\n"
+ "aus die kunst herum thut das leben sitzt dadrinne und meiner gestein mit euch stehn wir habt ihr euch ums ältste bitten die hölle schon darneben und meiner wach schleicht mag einen geist den wedel immer geschäftiger wirkenskraft und ganzes geisterzahn uns zeigte mir mich ein große loch in die\n"
]
}
],
@@ -1417,7 +1788,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
@@ -1433,22 +1804,22 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x105190898>"
+ "<AxesSubplot:>"
]
},
- "execution_count": 3,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 108x324 with 2 Axes>"
]
@@ -1479,7 +1850,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
@@ -1488,22 +1859,22 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x105190828>"
+ "<AxesSubplot:>"
]
},
- "execution_count": 5,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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bUMuNUke7dLd+yjfsM42j0WgKhtRAwZCg0RQMbV/UYNRsfEptRlOPITVo+WCY2Vwze97MtptZ4ZRPkmnpocTM2oAXgCuAncBTwDx33xq1YU2g1XuM2cB2d3/J3Q8Bj5DNDyYFWj0YpwK/qHi+M18mBVo9GDJMrR6MXcDbK55Pz5dJgVYPxlPAO8zsDDMbB3yMbH4wKdDS12O4e4+ZLQS+B7QBK919S+RmNYWW3l2V4Wv1oUSGScGQIAVDghQMCVIwJEjBkCAFQ4IUDAn6f3nRF5m+Gmr1AAAAAElFTkSuQmCC\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 108x324 with 2 Axes>"
]
@@ -1530,7 +1901,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
@@ -1539,7 +1910,7 @@
"927"
]
},
- "execution_count": 6,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -1562,7 +1933,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
@@ -1578,12 +1949,12 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
@@ -1621,7 +1992,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
@@ -1630,7 +2001,7 @@
"array([927., 397., 148., 929.])"
]
},
- "execution_count": 9,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
@@ -1659,7 +2030,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
@@ -1669,7 +2040,7 @@
" dtype=float128)"
]
},
- "execution_count": 10,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@@ -1698,7 +2069,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
@@ -1712,7 +2083,7 @@
" 3.99001076e+001]], dtype=float128)"
]
},
- "execution_count": 11,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@@ -1735,12 +2106,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
@@ -1768,7 +2139,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
@@ -1777,7 +2148,7 @@
"array([ 3.88079708, 4.0728263 , 45.26423912], dtype=float128)"
]
},
- "execution_count": 13,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -1792,22 +2163,22 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x122550908>"
+ "<AxesSubplot:>"
]
},
- "execution_count": 14,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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bqn0fVYyIsmhqdvdDyX+PAI8ANc02pmBE1NRU/aM7ZnammQ1s/zdwJaewXHeITj4jyuAQOxR4xEo77gP8u7s/WsuOFIyIejoY7r4X6JFZ0nMJRvbnt9LT8rgqKQQzm5VMUySnoDedfNZ8Td8b9aZgSBUUDAnqTcHQ+UUVes3Jp1SnN1UMqULDB8PMppjZC2b2kpmlzrovJQ19KDGzZuBF4KPAQWAzMMPdd0UdWB1o9IoxHnjJ3fe6ewuwErgm5TVC4wdjBPC7sq8PJtskRaMHQ2rU6ME4BLyr7OuRyTZJ0ejB2Aycb2bvMbN+wGcoLdEgKRr68xju3mpmNwD/BTQDy9z9+cjDqgsNfbkqtWv0Q4nUSMGQIAVDghQMCVIwJEjBkCAFQ4IUDAn6f2pfuv+WZd9BAAAAAElFTkSuQmCC\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 108x324 with 2 Axes>"
]
@@ -1856,14 +2227,14 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2.4.0\n"
+ "2.2.4-tf\n"
]
}
],
@@ -1954,14 +2325,16 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Model: \"functional_1\"\n",
+ "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n",
+ "553467904/553467096 [==============================] - 13s 0us/step\n",
+ "Model: \"model\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
@@ -2013,8 +2386,19 @@
"Trainable params: 134,260,544\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
- "None\n",
- "Extracted Features: 8091\n"
+ "None\n"
+ ]
+ },
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: 'Flicker8k_Dataset/'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-50-9545493bef71>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;31m# extract features from all images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mdirectory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Flicker8k_Dataset/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Extracted Features: %d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;31m# save to file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m<ipython-input-50-9545493bef71>\u001b[0m in \u001b[0;36mextract_features\u001b[0;34m(directory)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;31m# extract features from each photo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;31m# load an image from file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdirectory\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Flicker8k_Dataset/'"
]
}
],
@@ -2087,21 +2471,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1000268201_693b08cb0e.jpg#0\tA child in a pink dress is climbing up a set of stairs in an entry way .\n",
- "1000268201_693b08cb0e.jpg#1\tA girl going into a wooden building .\n",
- "1000268201_693b08cb0e.jpg#2\tA little girl climbing into a wooden playhouse .\n",
- "1000268201_693b08cb0e.jpg#3\tA little girl climbing the stairs to her playhouse .\n",
- "1000268201_693b08cb0e.jpg#4\tA little girl in a pink dress going into a wooden cabin .\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# load doc into memory\n",
"def load_doc(filename):\n",
@@ -2130,18 +2502,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['A child in a pink dress is climbing up a set of stairs in an entry way .', 'A girl going into a wooden building .', 'A little girl climbing into a wooden playhouse .', 'A little girl climbing the stairs to her playhouse .', 'A little girl in a pink dress going into a wooden cabin .']\n",
- "Loaded: 8092 \n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# extract descriptions for images\n",
"def load_descriptions(doc):\n",
@@ -2189,17 +2552,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['child in pink dress is climbing up set of stairs in an entry way', 'girl going into wooden building', 'little girl climbing into wooden playhouse', 'little girl climbing the stairs to her playhouse', 'little girl in pink dress going into wooden cabin']\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import string\n",
"def clean_descriptions(descriptions):\n",
@@ -2239,17 +2594,9 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Vocabulary Size: 8763\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# convert the loaded descriptions into a vocabulary of words\n",
"def to_vocabulary(descriptions):\n",
@@ -2276,7 +2623,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2357,7 +2704,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2401,7 +2748,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2440,7 +2787,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2462,19 +2809,9 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset: 6000\n",
- "Descriptions: train=6000\n",
- "Photos: train=6000\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from pickle import load\n",
"# load training dataset (6K)\n",
@@ -2511,17 +2848,9 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Vocabulary Size: 7579\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from keras.preprocessing.text import Tokenizer\n",
"# convert a dictionary of clean descriptions to a list of descriptions\n",
@@ -2583,7 +2912,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2621,7 +2950,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2640,21 +2969,9 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset: 6000\n",
- "Descriptions: train=6000\n",
- "Photos: train=6000\n",
- "Vocabulary Size: 7579\n",
- "Description Length: 34\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from keras.preprocessing.sequence import pad_sequences\n",
"from keras.utils import to_categorical\n",
@@ -2682,7 +2999,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2692,19 +3009,9 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset: 1000\n",
- "Descriptions: test=1000\n",
- "Photos: test=1000\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# dev dataset\n",
"\n",
@@ -2765,7 +3072,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2812,47 +3119,9 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"functional_5\"\n",
- "__________________________________________________________________________________________________\n",
- "Layer (type) Output Shape Param # Connected to \n",
- "==================================================================================================\n",
- "input_5 (InputLayer) [(None, 34)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "input_4 (InputLayer) [(None, 4096)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "embedding_1 (Embedding) (None, 34, 256) 1940224 input_5[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dropout_2 (Dropout) (None, 4096) 0 input_4[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dropout_3 (Dropout) (None, 34, 256) 0 embedding_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_3 (Dense) (None, 256) 1048832 dropout_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "lstm_1 (LSTM) (None, 256) 525312 dropout_3[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "add_1 (Add) (None, 256) 0 dense_3[0][0] \n",
- " lstm_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_4 (Dense) (None, 256) 65792 add_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_5 (Dense) (None, 7579) 1947803 dense_4[0][0] \n",
- "==================================================================================================\n",
- "Total params: 5,527,963\n",
- "Trainable params: 5,527,963\n",
- "Non-trainable params: 0\n",
- "__________________________________________________________________________________________________\n",
- "None\n",
- "('Failed to import pydot. You must `pip install pydot` and install graphviz (https://graphviz.gitlab.io/download/), ', 'for `pydotprint` to work.')\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# define the model\n",
"model = define_model(vocab_size, max_length)"
@@ -2890,7 +3159,7 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2910,106 +3179,9 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/20\n",
- "\n",
- "Epoch 00001: val_loss improved from inf to 4.05935, saving model to model-ep001-loss4.524-val_loss4.059.h5\n",
- "9576/9576 - 1003s - loss: 4.5237 - val_loss: 4.0593\n",
- "Epoch 2/20\n",
- "\n",
- "Epoch 00002: val_loss improved from 4.05935 to 3.89808, saving model to model-ep002-loss3.853-val_loss3.898.h5\n",
- "9576/9576 - 972s - loss: 3.8530 - val_loss: 3.8981\n",
- "Epoch 3/20\n",
- "\n",
- "Epoch 00003: val_loss improved from 3.89808 to 3.88203, saving model to model-ep003-loss3.644-val_loss3.882.h5\n",
- "9576/9576 - 966s - loss: 3.6441 - val_loss: 3.8820\n",
- "Epoch 4/20\n",
- "\n",
- "Epoch 00004: val_loss improved from 3.88203 to 3.87302, saving model to model-ep004-loss3.540-val_loss3.873.h5\n",
- "9576/9576 - 968s - loss: 3.5405 - val_loss: 3.8730\n",
- "Epoch 5/20\n",
- "\n",
- "Epoch 00005: val_loss did not improve from 3.87302\n",
- "9576/9576 - 965s - loss: 3.4794 - val_loss: 3.8927\n",
- "Epoch 6/20\n",
- "\n",
- "Epoch 00006: val_loss did not improve from 3.87302\n",
- "9576/9576 - 965s - loss: 3.4412 - val_loss: 3.9221\n",
- "Epoch 7/20\n",
- "\n",
- "Epoch 00007: val_loss did not improve from 3.87302\n",
- "9576/9576 - 965s - loss: 3.4173 - val_loss: 3.9663\n",
- "Epoch 8/20\n",
- "\n",
- "Epoch 00008: val_loss did not improve from 3.87302\n",
- "9576/9576 - 965s - loss: 3.4010 - val_loss: 3.9701\n",
- "Epoch 9/20\n",
- "\n",
- "Epoch 00009: val_loss did not improve from 3.87302\n",
- "9576/9576 - 966s - loss: 3.3896 - val_loss: 3.9799\n",
- "Epoch 10/20\n",
- "\n",
- "Epoch 00010: val_loss did not improve from 3.87302\n",
- "9576/9576 - 963s - loss: 3.3838 - val_loss: 3.9809\n",
- "Epoch 11/20\n",
- "\n",
- "Epoch 00011: val_loss did not improve from 3.87302\n",
- "9576/9576 - 959s - loss: 3.3742 - val_loss: 4.0341\n",
- "Epoch 12/20\n",
- "\n",
- "Epoch 00012: val_loss did not improve from 3.87302\n",
- "9576/9576 - 962s - loss: 3.3688 - val_loss: 4.0363\n",
- "Epoch 13/20\n",
- "\n",
- "Epoch 00013: val_loss did not improve from 3.87302\n",
- "9576/9576 - 961s - loss: 3.3642 - val_loss: 4.0589\n",
- "Epoch 14/20\n",
- "\n",
- "Epoch 00014: val_loss did not improve from 3.87302\n",
- "9576/9576 - 961s - loss: 3.3635 - val_loss: 4.0919\n",
- "Epoch 15/20\n",
- "\n",
- "Epoch 00015: val_loss did not improve from 3.87302\n",
- "9576/9576 - 964s - loss: 3.3631 - val_loss: 4.1155\n",
- "Epoch 16/20\n",
- "\n",
- "Epoch 00016: val_loss did not improve from 3.87302\n",
- "9576/9576 - 964s - loss: 3.3664 - val_loss: 4.1140\n",
- "Epoch 17/20\n",
- "\n",
- "Epoch 00017: val_loss did not improve from 3.87302\n",
- "9576/9576 - 969s - loss: 3.3702 - val_loss: 4.1086\n",
- "Epoch 18/20\n",
- "\n",
- "Epoch 00018: val_loss did not improve from 3.87302\n",
- "9576/9576 - 961s - loss: 3.3699 - val_loss: 4.1263\n",
- "Epoch 19/20\n",
- "\n",
- "Epoch 00019: val_loss did not improve from 3.87302\n",
- "9576/9576 - 961s - loss: 3.3772 - val_loss: 4.1310\n",
- "Epoch 20/20\n",
- "\n",
- "Epoch 00020: val_loss did not improve from 3.87302\n",
- "9576/9576 - 964s - loss: 3.3731 - val_loss: 4.1511\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<tensorflow.python.keras.callbacks.History at 0x158872d68>"
- ]
- },
- "execution_count": 66,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# fit model\n",
"model.fit([X1train, X2train], ytrain, epochs=20, verbose=2, callbacks=[checkpoint], validation_data=([X1test, X2test], ytest))"
@@ -3082,7 +3254,7 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -3131,7 +3303,7 @@
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -3176,27 +3348,9 @@
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset: 6000\n",
- "Descriptions: train=6000\n",
- "Vocabulary Size: 7579\n",
- "Description Length: 34\n",
- "Dataset: 1000\n",
- "Descriptions: test=1000\n",
- "Photos: test=1000\n",
- "BLEU-1: 0.537660\n",
- "BLEU-2: 0.284404\n",
- "BLEU-3: 0.190370\n",
- "BLEU-4: 0.087817\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from numpy import argmax\n",
"from pickle import load\n",
@@ -3403,18 +3557,9 @@
},
{
"cell_type": "code",
- "execution_count": 71,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset: 6000\n",
- "Descriptions: train=6000\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"from pickle import dump\n",
@@ -3604,17 +3749,9 @@
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "startseq man climbs rock face endseq\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from pickle import load\n",
"from numpy import argmax\n",
@@ -3778,7 +3915,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -3815,7 +3952,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -3853,7 +3990,7 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -3889,20 +4026,9 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(30000, 414113)"
- ]
- },
- "execution_count": 66,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"len(train_captions), len(all_captions)"
]
@@ -3923,7 +4049,7 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -3949,7 +4075,7 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -4103,7 +4229,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -4154,7 +4280,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -4267,71 +4393,9 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.embedding.embeddings\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.gru.state_spec\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.fc1.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.fc1.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.fc2.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.fc2.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).encoder.fc.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).encoder.fc.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.gru.cell.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.gru.cell.recurrent_kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.gru.cell.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.attention.W1.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.attention.W1.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.attention.W2.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.attention.W2.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.attention.V.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).decoder.attention.V.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.embedding.embeddings\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.fc1.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.fc1.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.fc2.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.fc2.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).encoder.fc.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).encoder.fc.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.gru.cell.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.gru.cell.recurrent_kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.gru.cell.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.W1.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.W1.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.W2.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.W2.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.V.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.V.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.embedding.embeddings\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.fc1.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.fc1.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.fc2.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.fc2.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).encoder.fc.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).encoder.fc.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.gru.cell.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.gru.cell.recurrent_kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.gru.cell.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.W1.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.W1.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.W2.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.W2.bias\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.V.kernel\n",
- "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.V.bias\n",
- "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"checkpoint_path = \"./checkpoints/train\"\n",
"ckpt = tf.train.Checkpoint(encoder=encoder,\n",
@@ -4363,161 +4427,9 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 2 Batch 0 Loss 0.8581\n",
- "Epoch 2 Batch 100 Loss 0.8342\n",
- "Epoch 2 Batch 200 Loss 0.7610\n",
- "Epoch 2 Batch 300 Loss 0.7606\n",
- "Epoch 2 Loss 0.796188\n",
- "Time taken for 1 epoch 982.328290939331 sec\n",
- "\n",
- "Epoch 3 Batch 0 Loss 0.7785\n",
- "Epoch 3 Batch 100 Loss 0.7223\n",
- "Epoch 3 Batch 200 Loss 0.7343\n",
- "Epoch 3 Batch 300 Loss 0.7207\n",
- "Epoch 3 Loss 0.725883\n",
- "Time taken for 1 epoch 958.1319239139557 sec\n",
- "\n",
- "Epoch 4 Batch 0 Loss 0.6941\n",
- "Epoch 4 Batch 100 Loss 0.7522\n",
- "Epoch 4 Batch 200 Loss 0.6781\n",
- "Epoch 4 Batch 300 Loss 0.6673\n",
- "Epoch 4 Loss 0.681024\n",
- "Time taken for 1 epoch 967.0104069709778 sec\n",
- "\n",
- "Epoch 5 Batch 0 Loss 0.5829\n",
- "Epoch 5 Batch 100 Loss 0.6536\n",
- "Epoch 5 Batch 200 Loss 0.6460\n",
- "Epoch 5 Batch 300 Loss 0.6390\n",
- "Epoch 5 Loss 0.645679\n",
- "Time taken for 1 epoch 945.3144369125366 sec\n",
- "\n",
- "Epoch 6 Batch 0 Loss 0.6242\n",
- "Epoch 6 Batch 100 Loss 0.6392\n",
- "Epoch 6 Batch 200 Loss 0.5987\n",
- "Epoch 6 Batch 300 Loss 0.5552\n",
- "Epoch 6 Loss 0.614216\n",
- "Time taken for 1 epoch 900.3698348999023 sec\n",
- "\n",
- "Epoch 7 Batch 0 Loss 0.6127\n",
- "Epoch 7 Batch 100 Loss 0.5677\n",
- "Epoch 7 Batch 200 Loss 0.6241\n",
- "Epoch 7 Batch 300 Loss 0.5243\n",
- "Epoch 7 Loss 0.584706\n",
- "Time taken for 1 epoch 899.3316700458527 sec\n",
- "\n",
- "Epoch 8 Batch 0 Loss 0.5852\n",
- "Epoch 8 Batch 100 Loss 0.5710\n",
- "Epoch 8 Batch 200 Loss 0.5524\n",
- "Epoch 8 Batch 300 Loss 0.5353\n",
- "Epoch 8 Loss 0.554990\n",
- "Time taken for 1 epoch 895.6873960494995 sec\n",
- "\n",
- "Epoch 9 Batch 0 Loss 0.5439\n",
- "Epoch 9 Batch 100 Loss 0.5284\n",
- "Epoch 9 Batch 200 Loss 0.5265\n",
- "Epoch 9 Batch 300 Loss 0.4904\n",
- "Epoch 9 Loss 0.527095\n",
- "Time taken for 1 epoch 895.4560778141022 sec\n",
- "\n",
- "Epoch 10 Batch 0 Loss 0.5039\n",
- "Epoch 10 Batch 100 Loss 0.5249\n",
- "Epoch 10 Batch 200 Loss 0.4992\n",
- "Epoch 10 Batch 300 Loss 0.5355\n",
- "Epoch 10 Loss 0.503663\n",
- "Time taken for 1 epoch 898.1137180328369 sec\n",
- "\n",
- "Epoch 11 Batch 0 Loss 0.4702\n",
- "Epoch 11 Batch 100 Loss 0.4801\n",
- "Epoch 11 Batch 200 Loss 0.4584\n",
- "Epoch 11 Batch 300 Loss 0.4521\n",
- "Epoch 11 Loss 0.471198\n",
- "Time taken for 1 epoch 899.4282419681549 sec\n",
- "\n",
- "Epoch 12 Batch 0 Loss 0.5028\n",
- "Epoch 12 Batch 100 Loss 0.4395\n",
- "Epoch 12 Batch 200 Loss 0.4221\n",
- "Epoch 12 Batch 300 Loss 0.4891\n",
- "Epoch 12 Loss 0.443868\n",
- "Time taken for 1 epoch 901.0765528678894 sec\n",
- "\n",
- "Epoch 13 Batch 0 Loss 0.4406\n",
- "Epoch 13 Batch 100 Loss 0.4189\n",
- "Epoch 13 Batch 200 Loss 0.4588\n",
- "Epoch 13 Batch 300 Loss 0.4045\n",
- "Epoch 13 Loss 0.421630\n",
- "Time taken for 1 epoch 902.8082618713379 sec\n",
- "\n",
- "Epoch 14 Batch 0 Loss 0.3844\n",
- "Epoch 14 Batch 100 Loss 0.4241\n",
- "Epoch 14 Batch 200 Loss 0.3811\n",
- "Epoch 14 Batch 300 Loss 0.4214\n",
- "Epoch 14 Loss 0.393005\n",
- "Time taken for 1 epoch 898.1368298530579 sec\n",
- "\n",
- "Epoch 15 Batch 0 Loss 0.3933\n",
- "Epoch 15 Batch 100 Loss 0.3787\n",
- "Epoch 15 Batch 200 Loss 0.3925\n",
- "Epoch 15 Batch 300 Loss 0.3499\n",
- "Epoch 15 Loss 0.367313\n",
- "Time taken for 1 epoch 897.7202432155609 sec\n",
- "\n",
- "Epoch 16 Batch 0 Loss 0.3794\n",
- "Epoch 16 Batch 100 Loss 0.3503\n",
- "Epoch 16 Batch 200 Loss 0.3256\n",
- "Epoch 16 Batch 300 Loss 0.3017\n",
- "Epoch 16 Loss 0.348995\n",
- "Time taken for 1 epoch 898.7975809574127 sec\n",
- "\n",
- "Epoch 17 Batch 0 Loss 0.3815\n",
- "Epoch 17 Batch 100 Loss 0.3719\n",
- "Epoch 17 Batch 200 Loss 0.2941\n",
- "Epoch 17 Batch 300 Loss 0.3606\n",
- "Epoch 17 Loss 0.326108\n",
- "Time taken for 1 epoch 897.0686180591583 sec\n",
- "\n",
- "Epoch 18 Batch 0 Loss 0.3249\n",
- "Epoch 18 Batch 100 Loss 0.3181\n",
- "Epoch 18 Batch 200 Loss 0.3206\n",
- "Epoch 18 Batch 300 Loss 0.3207\n",
- "Epoch 18 Loss 0.304019\n",
- "Time taken for 1 epoch 898.3756458759308 sec\n",
- "\n",
- "Epoch 19 Batch 0 Loss 0.3188\n",
- "Epoch 19 Batch 100 Loss 0.2909\n",
- "Epoch 19 Batch 200 Loss 0.2791\n",
- "Epoch 19 Batch 300 Loss 0.2855\n",
- "Epoch 19 Loss 0.284041\n",
- "Time taken for 1 epoch 897.6019468307495 sec\n",
- "\n",
- "Epoch 20 Batch 0 Loss 0.3313\n",
- "Epoch 20 Batch 100 Loss 0.2617\n",
- "Epoch 20 Batch 200 Loss 0.2656\n",
- "Epoch 20 Batch 300 Loss 0.2761\n",
- "Epoch 20 Loss 0.267829\n",
- "Time taken for 1 epoch 896.3991870880127 sec\n",
- "\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# adding this in a separate cell because if you run the training cell\n",
"# many times, the loss_plot array will be reset\n",
@@ -4599,30 +4511,9 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Real Caption: <start> a group of elephants in an outdoor enclosure <end>\n",
- "Prediction Caption: three elephants standing next to some elephants <end>\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 8 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"def evaluate(image):\n",
" attention_plot = np.zeros((max_length, attention_features_shape))\n",
@@ -4694,40 +4585,9 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Prediction Caption: a man in glasses and yellow tie <unk> <end>\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 9 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x600 at 0x167A47D68>"
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"image_url = 'https://tensorflow.org/images/surf.jpg'\n",
"image_extension = image_url[-4:]\n",
@@ -4743,29 +4603,9 @@
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Prediction Caption: a dog riding a horse over and skis <end>\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 9 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"image = './Bilder/example_10.jpg'\n",
"result, attention_plot = evaluate(image)\n",
@@ -4791,7 +4631,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.7.6"
}
},
"nbformat": 4,