{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Title :\n",
"Feed-Forward Neural Networks vs Convolution Neural Networks\n",
"\n",
"## Description :\n",
"The aim of this exercise is to train a feed-forward neural network and a convolutional neural network and compare the number of parameters between them on the following image classification task\n",
"\n",
"
\n",
"\n",
"## Instructions:\n",
"\n",
"- Since we have only one 'Pavlos' and one 'Not Pavlos' image, we will need to augment our dataset. We use an image generator to create 'translated' versions of our two images. The training is performed on these translated images given in the data folder.\n",
"\n",
"- Feed-Forward Neural Network:\n",
" - Build a simple Feed-Forward Neural Network and compile the model with binary cross entropy as the loss. \n",
" - Fit the model on the training data and save the history.\n",
" - Predict on the entire data. \n",
" - Visualize the loss and accuracy on train and validation data with respect to the epochs.\n",
"\n",
"- Convolutional Neural Network:\n",
" - Build a Convolution Neural Networks and compile the model with binary cross-entropy as the loss. \n",
" - Fit the model on the training data and save the history.\n",
" - Predict on the entire data. \n",
" - Visualize the loss and accuracy on train and validation data with respect to the epochs.\n",
"\n",
"- Compare the accuracy and the number of parameters of both the models.\n",
"\n",
"## Hints: \n",
"\n",
"keras.Sequential()Creates a sequential model. A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.\n",
"\n",
"keras.compile()Configures the model for training.\n",
"\n",
"keras.fit()Trains the model for a fixed number of epochs.\n",
"\n",
"history.history[]The returned \"history\" object from model.fit() holds a dictionary of the loss values and metric values during training.\n",
"\n",
"keras.evaluate()Returns the loss value & metrics values for the model in test mode.\n",
"\n",
"tf.keras.preprocessing.image.ImageDataGenerator()Generate batches of tensor image data with real-time data augmentation. This function is used in our helper code. \n",
"\n",
"tf.keras.layers.Flatten()Flattens the input. Does not affect the batch size.\n",
"\n",
"tf.keras.layers.Conv2D()2D convolution layer (e.g. spatial convolution over images).\n",
"\n",
"tf.keras.layers.Dense()A regular densely-connected NN layer.\n",
"\n",
"\n",
"NOTE - The accuracy testing is done on the original network. Ensure to reset to the original parameters after answering the pause and think questions to pass the tests."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image Classification: FFNN vs CNN"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Importing necessary libraries\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from numpy.random import seed\n",
"seed(1)\n",
"tf.random.set_seed(1)\n",
"import os\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Input\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"from keras.preprocessing.image import img_to_array\n",
"from keras.preprocessing.image import load_img\n",
"from keras.preprocessing.image import ImageDataGenerator \n",
"from PIL import Image\n",
"from numpy import asarray\n",
"from helper import plot_history"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 100 images belonging to 2 classes.\n",
"Generator produces images of size (150, 150) (with 3 color channels)\n",
"Images are generated in batches of size 16\n"
]
}
],
"source": [
"# Initialise an image generator object\n",
"generator = ImageDataGenerator(rescale=1./255)\n",
"\n",
"# Initialising number of data images\n",
"num_data = len(os.listdir('data/pavlos') + os.listdir('data/not_pavlos'))\n",
"\n",
"# Read the image data from the directory using the generator object\n",
"img_generator = generator.flow_from_directory(directory=\"data/\", color_mode='rgb', seed=1,\n",
" batch_size=16, target_size=(150, 150), class_mode='binary')\n",
"\n",
"# Print the target size i.e. the total dataset size\n",
"TARGET_SIZE = img_generator.target_size\n",
"print(f'Generator produces images of size {TARGET_SIZE} (with 3 color channels)')\n",
"\n",
"# Print the batch size\n",
"BATCH_SIZE = img_generator.batch_size\n",
"print(f'Images are generated in batches of size {BATCH_SIZE}')\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plotting a sample of the generated images \n",
"sample_batch = img_generator.next()[0]\n",
"fig, ax = plt.subplots(4,4)\n",
"ax = ax.ravel()\n",
"for i, img in enumerate(sample_batch):\n",
" ax[i].set_axis_off()\n",
" ax[i].imshow(img)\n",
"plt.suptitle('Sample Batch of Generated Images', y=1.05)\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Feed-Forward Neural Network\n",
"\n",
"Our first network will be a feed-forward neural network. The only layers with learned parameters we will be using are dense layers."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Fixing the random seed\n",
"seed(1)\n",
"tf.random.set_seed(1)\n",
"\n",
"# Creating a feed-forward Neural Network\n",
"FFNN = Sequential()\n",
"\n",
"# Specify a layer that takes the input with input shape\n",
"# the same as the size of the image defined during image generation\n",
"# Remember to take into account that the image has 3 channels\n",
"FFNN.add(tf.keras.layers.Input(shape=(150,150, 3)))\n",
"\n",
"# Add a flatten layer to enable FFNN to process images\n",
"FFNN.add(tf.keras.layers.Flatten())\n",
"\n",
"# Specify a list of the number of nodes for each dense layer\n",
"ffnn_filters = [6,4,2]\n",
"\n",
"# Add dense layers for the number of nodes in ffnn_filters with ReLU activation\n",
"for n_nodes in ffnn_filters:\n",
" FFNN.add(tf.keras.layers.Dense(n_nodes, activation='relu'))\n",
"\n",
"# Add the final dense layer with 1 output node to differentiate \n",
"# between the two classes and sigmoid activation\n",
"FFNN.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
"\n",
"# Compile the model with bce as the loss, accuracy as the metric and adam optimizer\n",
"FFNN.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer='adam')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_5\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"flatten_3 (Flatten) (None, 67500) 0 \n",
"_________________________________________________________________\n",
"dense_10 (Dense) (None, 6) 405006 \n",
"_________________________________________________________________\n",
"dense_11 (Dense) (None, 4) 28 \n",
"_________________________________________________________________\n",
"dense_12 (Dense) (None, 2) 10 \n",
"_________________________________________________________________\n",
"dense_13 (Dense) (None, 1) 3 \n",
"=================================================================\n",
"Total params: 405,047\n",
"Trainable params: 405,047\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# Print a summary of the model and observe the total number of parameters\n",
"FFNN.summary()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"\r",
"1/6 [====>.........................] - ETA: 2s - loss: 0.6810 - accuracy: 0.5625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"4/6 [===================>..........] - ETA: 0s - loss: 1.5804 - accuracy: 0.5482\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 1s 46ms/step - loss: 1.4907 - accuracy: 0.5323 - val_loss: 0.6934 - val_accuracy: 0.4375\n",
"Epoch 2/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6931 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5177\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 19ms/step - loss: 0.6931 - accuracy: 0.5182 - val_loss: 0.6932 - val_accuracy: 0.5000\n",
"Epoch 3/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6932 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5014\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 19ms/step - loss: 0.6931 - accuracy: 0.5012 - val_loss: 0.6932 - val_accuracy: 0.5000\n",
"Epoch 4/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6922 - accuracy: 0.6250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6930 - accuracy: 0.5189\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 20ms/step - loss: 0.6931 - accuracy: 0.5132 - val_loss: 0.6921 - val_accuracy: 0.6250\n",
"Epoch 5/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6937 - accuracy: 0.4375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6933 - accuracy: 0.4813\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 19ms/step - loss: 0.6933 - accuracy: 0.4854 - val_loss: 0.6937 - val_accuracy: 0.4375\n",
"Epoch 6/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6921 - accuracy: 0.6250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6928 - accuracy: 0.5477\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 19ms/step - loss: 0.6928 - accuracy: 0.5424 - val_loss: 0.6921 - val_accuracy: 0.6250\n",
"Epoch 7/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6932 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6932 - accuracy: 0.4981\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 19ms/step - loss: 0.6932 - accuracy: 0.4999 - val_loss: 0.6932 - val_accuracy: 0.5000\n",
"Epoch 8/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6915 - accuracy: 0.6875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6928 - accuracy: 0.5401\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 20ms/step - loss: 0.6929 - accuracy: 0.5344 - val_loss: 0.6914 - val_accuracy: 0.6875\n",
"Epoch 9/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6937 - accuracy: 0.4375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"3/6 [==============>...............] - ETA: 0s - loss: 0.6938 - accuracy: 0.4340\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 26ms/step - loss: 0.6936 - accuracy: 0.4563 - val_loss: 0.6937 - val_accuracy: 0.4375\n",
"Epoch 10/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6937 - accuracy: 0.4375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6933 - accuracy: 0.4892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 21ms/step - loss: 0.6933 - accuracy: 0.4893 - val_loss: 0.6937 - val_accuracy: 0.4375\n"
]
}
],
"source": [
"# Train the model\n",
"FFNN_history = FFNN.fit(\n",
" img_generator,\n",
" steps_per_epoch=num_data// BATCH_SIZE,\n",
" epochs=10, shuffle=False, workers=0,\n",
" validation_data=img_generator,\n",
" validation_steps=num_data*0.25// BATCH_SIZE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ⏸ Enter the number of parameters in the given FFNN architecture."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"### edTest(test_chow1) ###\n",
"\n",
"# Enter the answer by typing in a number in the space provided\n",
"answer1 = '405,047'"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Use the plot history function from the helper file to plot the data\n",
"plot_history(FFNN_history, 'Feed-Forward Neural Network')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"1/2 [==============>...............] - ETA: 0s - loss: 0.6915 - accuracy: 0.6875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"2/2 [==============================] - 0s 12ms/step - loss: 0.6913 - accuracy: 0.7188\n",
"FFNN Accuracy: 0.71875\n"
]
}
],
"source": [
"### edTest(test_ffnn_acc) ###\n",
"\n",
"# Evaluate your model\n",
"FFNN_loss, FFNN_acc = FFNN.evaluate(img_generator, steps=2)\n",
"print(f'FFNN Accuracy: {FFNN_acc}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ⏸ Alter the network architecture by increasing the number of nodes and/or layers. Enter the number of parameters of the network that gives a validation accuracy of above 80%."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"### edTest(test_chow2) ###\n",
"\n",
"# Enter the answer by typing in a number in the space provided\n",
"answer2 = '405,047'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convolutional Neural Network"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"### edTest(test_cnn_count_param) ###\n",
"\n",
"# Fixing the random seed\n",
"seed(1)\n",
"tf.random.set_seed(1)\n",
"\n",
"# Creating a CNN\n",
"CNN = Sequential()\n",
"\n",
"# Add a layer to take the input with shape (150,150,3)\n",
"CNN.add(Input(shape=(150, 150, 3)))\n",
"\n",
"# Specify a list of the number of filters for each convolutional layer\n",
"cnn_filters = [8,8,8,8,8]\n",
"\n",
"# Add convolutional layers with number of filters in cnn_filters\n",
"# with kernel size as 3, stride of 2 and relu activation\n",
"for n_filters in cnn_filters:\n",
" CNN.add(Conv2D(n_filters,strides=(2, 2), kernel_size=3, activation='relu'))\n",
"\n",
"# Add the flatten layer between the CNN and dense layer\n",
"CNN.add(Flatten())\n",
"\n",
"# Add a dense layer with 64 nodes and relu activation\n",
"CNN.add(Dense(64, activation='relu'))\n",
" \n",
"# Specify the output layer with sigmoid activation and one node\n",
"CNN.add(Dense(1, activation='sigmoid'))\n",
" \n",
"# Compile the model with bce as the loss, accuracy as the metric and adam optimizer\n",
"CNN.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer='adam')\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_7\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_15 (Conv2D) (None, 74, 74, 8) 224 \n",
"_________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 36, 36, 8) 584 \n",
"_________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, 17, 17, 8) 584 \n",
"_________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 8, 8, 8) 584 \n",
"_________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 3, 3, 8) 584 \n",
"_________________________________________________________________\n",
"flatten_5 (Flatten) (None, 72) 0 \n",
"_________________________________________________________________\n",
"dense_16 (Dense) (None, 64) 4672 \n",
"_________________________________________________________________\n",
"dense_17 (Dense) (None, 1) 65 \n",
"=================================================================\n",
"Total params: 7,297\n",
"Trainable params: 7,297\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# Print a summary of the model and observe the total number of parameters\n",
"CNN.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ⏸ Enter the number of parameters in the given CNN architecture.\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"### edTest(test_chow3) ###\n",
"\n",
"# Enter the answer by typing in a number in the space provided\n",
"answer3 = '7,297'"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"\r",
"1/6 [====>.........................] - ETA: 2s - loss: 0.6943 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"5/6 [========================>.....] - ETA: 0s - loss: 0.6940 - accuracy: 0.4173\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 1s 50ms/step - loss: 0.6937 - accuracy: 0.4350 - val_loss: 0.6906 - val_accuracy: 0.6250\n",
"Epoch 2/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6895 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"5/6 [========================>.....] - ETA: 0s - loss: 0.6900 - accuracy: 0.6885\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 23ms/step - loss: 0.6899 - accuracy: 0.6763 - val_loss: 0.6850 - val_accuracy: 0.7500\n",
"Epoch 3/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6839 - accuracy: 0.8750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"2/6 [=========>....................] - ETA: 0s - loss: 0.6843 - accuracy: 0.8594\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.6836 - accuracy: 0.8774\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 33ms/step - loss: 0.6834 - accuracy: 0.8830 - val_loss: 0.6754 - val_accuracy: 1.0000\n",
"Epoch 4/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6788 - accuracy: 0.9375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"5/6 [========================>.....] - ETA: 0s - loss: 0.6733 - accuracy: 0.9546\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 23ms/step - loss: 0.6721 - accuracy: 0.9557 - val_loss: 0.6581 - val_accuracy: 0.9375\n",
"Epoch 5/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6523 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"5/6 [========================>.....] - ETA: 0s - loss: 0.6477 - accuracy: 0.9815\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 23ms/step - loss: 0.6460 - accuracy: 0.9808 - val_loss: 0.6095 - val_accuracy: 1.0000\n",
"Epoch 6/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.6062 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"3/6 [==============>...............] - ETA: 0s - loss: 0.5991 - accuracy: 0.9826\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"4/6 [===================>..........] - ETA: 0s - loss: 0.5966 - accuracy: 0.9831\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"5/6 [========================>.....] - ETA: 0s - loss: 0.5952 - accuracy: 0.9840\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - ETA: 0s - loss: 0.5930 - accuracy: 0.9832\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 91ms/step - loss: 0.5915 - accuracy: 0.9826 - val_loss: 0.5142 - val_accuracy: 1.0000\n",
"Epoch 7/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.5194 - accuracy: 0.9375\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"4/6 [===================>..........] - ETA: 0s - loss: 0.5019 - accuracy: 0.9674\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 27ms/step - loss: 0.4830 - accuracy: 0.9737 - val_loss: 0.3584 - val_accuracy: 1.0000\n",
"Epoch 8/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.3692 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"5/6 [========================>.....] - ETA: 0s - loss: 0.3215 - accuracy: 0.9902\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 24ms/step - loss: 0.3068 - accuracy: 0.9900 - val_loss: 0.1658 - val_accuracy: 1.0000\n",
"Epoch 9/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.1456 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"5/6 [========================>.....] - ETA: 0s - loss: 0.1274 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 23ms/step - loss: 0.1193 - accuracy: 1.0000 - val_loss: 0.0316 - val_accuracy: 1.0000\n",
"Epoch 10/10\n",
"\r",
"1/6 [====>.........................] - ETA: 0s - loss: 0.0401 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"5/6 [========================>.....] - ETA: 0s - loss: 0.0408 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"6/6 [==============================] - 0s 23ms/step - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.0097 - val_accuracy: 1.0000\n"
]
}
],
"source": [
"# Fit the model on the image generator\n",
"CNN_history = CNN.fit(\n",
" img_generator,\n",
" steps_per_epoch=num_data // BATCH_SIZE,\n",
" epochs=10, shuffle=False, workers=0,\n",
" validation_data=img_generator,\n",
" validation_steps=num_data*0.25// BATCH_SIZE)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the history of the model\n",
"plot_history(CNN_history, 'Convolutional Neural Network')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"1/2 [==============>...............] - ETA: 0s - loss: 0.0064 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"2/2 [==============================] - 0s 15ms/step - loss: 0.0153 - accuracy: 1.0000\n",
"CNN Test Accuracy: 1.0\n"
]
}
],
"source": [
"### edTest(test_cnn_acc) ###\n",
"\n",
"# Evaluate the model on the entire data\n",
"CNN_loss, CNN_acc = CNN.evaluate(img_generator, steps=2)\n",
"print(f'CNN Test Accuracy: {CNN_acc}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ⏸ Remove the last convolution layer in the Convolution Neural Network defined above. How does this affect the number of parameters?\n",
"\n",
"#### A. The number of parameters decrease.\n",
"#### B. The number of parameters increase.\n",
"#### C. The number of parameters remains the same. \n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"### edTest(test_chow4) ###\n",
"\n",
"# Enter the answer by typing in a number in the space provided\n",
"answer4 = 'A'"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}