diff --git a/notebooks/Block_4/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb b/notebooks/Block_4/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb
index 3698ac8cd45e3eeab252ccb8c8ba4069d770bf16..30e328353280edb9f065e13aaf9e43fb3a74083a 100644
--- a/notebooks/Block_4/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb
+++ b/notebooks/Block_4/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Part 1 - Convolutional Neural Networks for CIFAR-10\n",
+ "# Part 1.1 - Convolutional Neural Networks for CIFAR-10\n",
"\n",
"\n",
"In this notebook chapter, we'll build, train and optimize a neural network to classify images of the CIFAR-10 dataset using convolutional neural networks.\n",
@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
- "execution_count": 98,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -22,9 +22,6 @@
"output_type": "stream",
"text": [
"2.7.1\n",
- "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
- "170500096/170498071 [==============================] - 7s 0us/step\n",
- "170508288/170498071 [==============================] - 7s 0us/step\n",
"Train: X=(50000, 32, 32, 3), y=(50000, 1)\n",
"Test: X=(10000, 32, 32, 3), y=(10000, 1)\n",
"(50000, 1)\n",
@@ -82,7 +79,7 @@
},
{
"cell_type": "code",
- "execution_count": 99,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -122,7 +119,7 @@
},
{
"cell_type": "code",
- "execution_count": 100,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -201,7 +198,7 @@
},
{
"cell_type": "code",
- "execution_count": 101,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -260,7 +257,7 @@
},
{
"cell_type": "code",
- "execution_count": 102,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -271,16 +268,16 @@
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
- " conv2d_4 (Conv2D) (None, 32, 32, 32) 896 \n",
+ " conv2d_8 (Conv2D) (None, 32, 32, 32) 896 \n",
" \n",
- " batch_normalization_4 (Batc (None, 32, 32, 32) 128 \n",
+ " batch_normalization_8 (Batc (None, 32, 32, 32) 128 \n",
" hNormalization) \n",
" \n",
" activation (Activation) (None, 32, 32, 32) 0 \n",
" \n",
- " conv2d_5 (Conv2D) (None, 30, 30, 32) 9248 \n",
+ " conv2d_9 (Conv2D) (None, 30, 30, 32) 9248 \n",
" \n",
- " batch_normalization_5 (Batc (None, 30, 30, 32) 128 \n",
+ " batch_normalization_9 (Batc (None, 30, 30, 32) 128 \n",
" hNormalization) \n",
" \n",
" activation_1 (Activation) (None, 30, 30, 32) 0 \n",
@@ -290,17 +287,17 @@
" \n",
" dropout (Dropout) (None, 15, 15, 32) 0 \n",
" \n",
- " conv2d_6 (Conv2D) (None, 15, 15, 64) 18496 \n",
+ " conv2d_10 (Conv2D) (None, 15, 15, 64) 18496 \n",
" \n",
- " batch_normalization_6 (Batc (None, 15, 15, 64) 256 \n",
- " hNormalization) \n",
+ " batch_normalization_10 (Bat (None, 15, 15, 64) 256 \n",
+ " chNormalization) \n",
" \n",
" activation_2 (Activation) (None, 15, 15, 64) 0 \n",
" \n",
- " conv2d_7 (Conv2D) (None, 13, 13, 64) 36928 \n",
+ " conv2d_11 (Conv2D) (None, 13, 13, 64) 36928 \n",
" \n",
- " batch_normalization_7 (Batc (None, 13, 13, 64) 256 \n",
- " hNormalization) \n",
+ " batch_normalization_11 (Bat (None, 13, 13, 64) 256 \n",
+ " chNormalization) \n",
" \n",
" activation_3 (Activation) (None, 13, 13, 64) 0 \n",
" \n",
@@ -313,8 +310,8 @@
" \n",
" dense (Dense) (None, 512) 1180160 \n",
" \n",
- " batch_normalization_8 (Batc (None, 512) 2048 \n",
- " hNormalization) \n",
+ " batch_normalization_12 (Bat (None, 512) 2048 \n",
+ " chNormalization) \n",
" \n",
" activation_4 (Activation) (None, 512) 0 \n",
" \n",
@@ -322,8 +319,8 @@
" \n",
" dense_1 (Dense) (None, 10) 5130 \n",
" \n",
- " batch_normalization_9 (Batc (None, 10) 40 \n",
- " hNormalization) \n",
+ " batch_normalization_13 (Bat (None, 10) 40 \n",
+ " chNormalization) \n",
" \n",
" activation_5 (Activation) (None, 10) 0 \n",
" \n",
@@ -764,7 +761,7 @@
"id": "FE7KNzPPVrVV"
},
"source": [
- "# Part 2 - Dogs vs Cats Image Classification : Visualizing what ConvNets learn?"
+ "# Part 1.2 - Dogs vs Cats Image Classification : Visualizing what ConvNets learn?"
]
},
{