From 109acba21df97ac673438fc2cebe05cf1e5eab11 Mon Sep 17 00:00:00 2001 From: Mirko Birbaumer <mirko.birbaumer@hslu.ch> Date: Sun, 24 Oct 2021 10:14:34 +0000 Subject: [PATCH] downgraded hpy package --- ... - Object Detection and Segmentation.ipynb | 102 +++++++++--------- 1 file changed, 53 insertions(+), 49 deletions(-) diff --git a/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb b/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb index 632a2ee..78819a6 100644 --- a/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb +++ b/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb @@ -38,11 +38,13 @@ "Found 2 image links\n", "Saved 2 images\n", "Found 2 image links\n", - "Saved 2 images\n", + "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/thumb/b/bd/1990_Venice_Film_Festival_Robert_De_Niro.jpg/1200px-1990_Venice_Film_Festival_Robert_De_Niro.jpg - cannot identify image file <_io.BytesIO object at 0x7f4f951b6b30>\n", + "Saved 1 images\n", "Found 2 image links\n", "Saved 2 images\n", "Found 2 image links\n", - "Saved 2 images\n", + "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/1/15/Sandra_Bullock_in_July_2013.jpg - cannot identify image file <_io.BytesIO object at 0x7f4f950e8410>\n", + "Saved 1 images\n", "Found 2 image links\n", "Saved 2 images\n", "Found 2 image links\n", @@ -95,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", @@ -131,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", @@ -143,20 +145,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 0 images belonging to 8 classes.\n" + "Found 18 images belonging to 8 classes.\n" ] }, { - "ename": "ValueError", - "evalue": "Asked to retrieve element 0, but the Sequence has length 0", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-4-a6b4e6ab5ab7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 24\u001b[0m batch_size=25, class_mode='sparse', shuffle=False)\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mplot_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdir_iter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m...\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 27\u001b[0m \u001b[0mdir_iter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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/keras_preprocessing/image/iterator.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;34m'but the Sequence '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m 'has length {length}'.format(idx=idx,\n\u001b[0;32m---> 57\u001b[0;31m length=len(self)))\n\u001b[0m\u001b[1;32m 58\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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 59\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_batches_seen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Asked to retrieve element 0, but the Sequence has length 0" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0., 0., 1., 1., 1., 1., 2., 2., 3., 4., 4., 5., 5., 6., 6., 7., 7.,\n", + " 7.], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -242,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", @@ -284,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", @@ -332,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", @@ -350,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", @@ -362,17 +375,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:sample_weight modes were coerced from\n", - " ...\n", - " to \n", - " ['...']\n", - "WARNING:tensorflow:sample_weight modes were coerced from\n", - " ...\n", - " to \n", - " ['...']\n", - "Train for 24 steps, validate for 4 steps\n", "Epoch 1/20\n", - " 2/24 [=>............................] - ETA: 16s - loss: 2.4319 - accuracy: 0.1000" + " 5/24 [=====>........................] - ETA: 9s - loss: 2.0860 - accuracy: 0.1055 " ] }, { @@ -387,45 +391,45 @@ "name": "stdout", "output_type": "stream", "text": [ - "24/24 [==============================] - 10s 398ms/step - loss: 2.1400 - accuracy: 0.1125 - val_loss: 2.0787 - val_accuracy: 0.2000\n", + "24/24 [==============================] - 15s 582ms/step - loss: 2.0910 - accuracy: 0.1094 - val_loss: 2.0783 - val_accuracy: 0.1375\n", "Epoch 2/20\n", - "24/24 [==============================] - 9s 363ms/step - loss: 2.0715 - accuracy: 0.1688 - val_loss: 2.0529 - val_accuracy: 0.2250\n", + "24/24 [==============================] - 13s 553ms/step - loss: 2.0796 - accuracy: 0.1347 - val_loss: 2.0725 - val_accuracy: 0.1750\n", "Epoch 3/20\n", - "24/24 [==============================] - 8s 347ms/step - loss: 2.0450 - accuracy: 0.1708 - val_loss: 2.0063 - val_accuracy: 0.2250\n", + "24/24 [==============================] - 14s 537ms/step - loss: 2.0719 - accuracy: 0.1885 - val_loss: 2.0578 - val_accuracy: 0.2750\n", "Epoch 4/20\n", - "24/24 [==============================] - 8s 353ms/step - loss: 1.9824 - accuracy: 0.2104 - val_loss: 1.9108 - val_accuracy: 0.2250\n", + "24/24 [==============================] - 12s 507ms/step - loss: 2.0666 - accuracy: 0.1690 - val_loss: 1.9938 - val_accuracy: 0.2625\n", "Epoch 5/20\n", - "24/24 [==============================] - 8s 342ms/step - loss: 1.9082 - accuracy: 0.2438 - val_loss: 1.8576 - val_accuracy: 0.3250\n", + "24/24 [==============================] - 12s 493ms/step - loss: 2.0107 - accuracy: 0.1980 - val_loss: 1.8511 - val_accuracy: 0.2750\n", "Epoch 6/20\n", - "24/24 [==============================] - 8s 347ms/step - loss: 1.8140 - accuracy: 0.3167 - val_loss: 1.7524 - val_accuracy: 0.3875\n", + "24/24 [==============================] - 13s 542ms/step - loss: 1.9818 - accuracy: 0.2149 - val_loss: 1.7824 - val_accuracy: 0.4500\n", "Epoch 7/20\n", - "24/24 [==============================] - 8s 346ms/step - loss: 1.7485 - accuracy: 0.3271 - val_loss: 1.6948 - val_accuracy: 0.3875\n", + "24/24 [==============================] - 13s 521ms/step - loss: 1.8674 - accuracy: 0.2838 - val_loss: 1.8241 - val_accuracy: 0.3875\n", "Epoch 8/20\n", - "24/24 [==============================] - 9s 355ms/step - loss: 1.6554 - accuracy: 0.3812 - val_loss: 1.5996 - val_accuracy: 0.4500\n", + "24/24 [==============================] - 13s 510ms/step - loss: 1.8343 - accuracy: 0.2846 - val_loss: 1.7613 - val_accuracy: 0.3875\n", "Epoch 9/20\n", - "24/24 [==============================] - 9s 355ms/step - loss: 1.5621 - accuracy: 0.4083 - val_loss: 1.5193 - val_accuracy: 0.5000\n", + "24/24 [==============================] - 14s 552ms/step - loss: 1.7777 - accuracy: 0.2865 - val_loss: 1.6303 - val_accuracy: 0.4125\n", "Epoch 10/20\n", - "24/24 [==============================] - 8s 353ms/step - loss: 1.5562 - accuracy: 0.3812 - val_loss: 1.5248 - val_accuracy: 0.4500\n", + "24/24 [==============================] - 14s 582ms/step - loss: 1.6328 - accuracy: 0.3762 - val_loss: 1.6302 - val_accuracy: 0.4625\n", "Epoch 11/20\n", - "24/24 [==============================] - 8s 335ms/step - loss: 1.5109 - accuracy: 0.4125 - val_loss: 1.5640 - val_accuracy: 0.4375\n", + "24/24 [==============================] - 14s 559ms/step - loss: 1.5805 - accuracy: 0.4026 - val_loss: 1.7460 - val_accuracy: 0.3625\n", "Epoch 12/20\n", - "24/24 [==============================] - 8s 340ms/step - loss: 1.4337 - accuracy: 0.4583 - val_loss: 1.5276 - val_accuracy: 0.4625\n", + "24/24 [==============================] - 14s 560ms/step - loss: 1.5931 - accuracy: 0.3879 - val_loss: 1.6343 - val_accuracy: 0.4000\n", "Epoch 13/20\n", - "24/24 [==============================] - 8s 353ms/step - loss: 1.3907 - accuracy: 0.4875 - val_loss: 1.5706 - val_accuracy: 0.4125\n", + "24/24 [==============================] - 13s 533ms/step - loss: 1.5460 - accuracy: 0.4395 - val_loss: 1.5797 - val_accuracy: 0.4875\n", "Epoch 14/20\n", - "24/24 [==============================] - 8s 330ms/step - loss: 1.3740 - accuracy: 0.4563 - val_loss: 1.5009 - val_accuracy: 0.4500\n", + "24/24 [==============================] - 14s 541ms/step - loss: 1.4899 - accuracy: 0.4612 - val_loss: 1.6318 - val_accuracy: 0.4125\n", "Epoch 15/20\n", - "24/24 [==============================] - 8s 350ms/step - loss: 1.3238 - accuracy: 0.4979 - val_loss: 1.4842 - val_accuracy: 0.4250\n", + "24/24 [==============================] - 14s 573ms/step - loss: 1.4495 - accuracy: 0.4404 - val_loss: 1.5812 - val_accuracy: 0.4500\n", "Epoch 16/20\n", - "24/24 [==============================] - 8s 339ms/step - loss: 1.3219 - accuracy: 0.4917 - val_loss: 1.4689 - val_accuracy: 0.4750\n", + "24/24 [==============================] - 14s 560ms/step - loss: 1.3668 - accuracy: 0.4989 - val_loss: 1.6858 - val_accuracy: 0.4250\n", "Epoch 17/20\n", - "24/24 [==============================] - 8s 336ms/step - loss: 1.2339 - accuracy: 0.5188 - val_loss: 1.4910 - val_accuracy: 0.5000\n", + "24/24 [==============================] - 15s 587ms/step - loss: 1.3437 - accuracy: 0.4949 - val_loss: 1.4865 - val_accuracy: 0.4125\n", "Epoch 18/20\n", - "24/24 [==============================] - 8s 343ms/step - loss: 1.3074 - accuracy: 0.5000 - val_loss: 1.4635 - val_accuracy: 0.4625\n", + "24/24 [==============================] - 14s 556ms/step - loss: 1.2471 - accuracy: 0.5492 - val_loss: 1.5927 - val_accuracy: 0.4500\n", "Epoch 19/20\n", - "24/24 [==============================] - 8s 349ms/step - loss: 1.2370 - accuracy: 0.5167 - val_loss: 1.4229 - val_accuracy: 0.4625\n", + "24/24 [==============================] - 14s 559ms/step - loss: 1.2935 - accuracy: 0.5065 - val_loss: 1.5874 - val_accuracy: 0.4375\n", "Epoch 20/20\n", - "24/24 [==============================] - 8s 340ms/step - loss: 1.1461 - accuracy: 0.5521 - val_loss: 1.4321 - val_accuracy: 0.4875\n" + "24/24 [==============================] - 14s 567ms/step - loss: 1.2058 - accuracy: 0.5108 - val_loss: 1.5376 - val_accuracy: 0.4500\n" ] } ], -- GitLab