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Merge pull request #21 from garg-saurav/master
Fixed #1 Update architecture to resemble LeNet-5
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{ | ||
"nbformat": 4, | ||
"nbformat_minor": 0, | ||
"metadata": { | ||
"colab": { | ||
"name": "MNIST_CNN.ipynb", | ||
"provenance": [], | ||
"collapsed_sections": [] | ||
}, | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"name": "python3" | ||
}, | ||
"accelerator": "GPU" | ||
}, | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"id": "3wF5wszaj97Y" | ||
}, | ||
"source": [ | ||
"# MNIST using Neural Networks" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"id": "nnrWf3PCEzXL" | ||
}, | ||
"source": [ | ||
"Run the statement to import the TensorFlow library:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"metadata": { | ||
"id": "0trJmd6DjqBZ" | ||
}, | ||
"source": [ | ||
"import tensorflow as tf" | ||
], | ||
"execution_count": 1, | ||
"outputs": [] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"id": "7NAbSZiaoJ4z" | ||
}, | ||
"source": [ | ||
"Run the statement to load and prepare the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) and to convert the samples from integers to floating-point numbers:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"metadata": { | ||
"id": "7FP5258xjs-v" | ||
}, | ||
"source": [ | ||
"mnist = tf.keras.datasets.mnist\n", | ||
"\n", | ||
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", | ||
"x_train, x_test = x_train / 255.0, x_test / 255.0" | ||
], | ||
"execution_count": 2, | ||
"outputs": [] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"id": "BPZ68wASog_I" | ||
}, | ||
"source": [ | ||
"Build the model with multiple convolution, pooling, dense layers and relu activation function:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"metadata": { | ||
"id": "h3IKyzTCDNGo" | ||
}, | ||
"source": [ | ||
"model = tf.keras.models.Sequential([\n", | ||
" tf.keras.layers.Reshape((28,28,1), input_shape=(28,28)),\n", | ||
" tf.keras.layers.Conv2D(5, (5,5), activation='relu'),\n", | ||
" tf.keras.layers.MaxPool2D((2,2)),\n", | ||
" tf.keras.layers.Conv2D(15, (5,5), activation='relu'),\n", | ||
" tf.keras.layers.MaxPool2D((2,2)), \n", | ||
" tf.keras.layers.Flatten(),\n", | ||
" tf.keras.layers.Dense(120, activation='relu'), \n", | ||
" tf.keras.layers.Dense(80, activation='relu'), \n", | ||
" tf.keras.layers.Dense(40, activation='relu'), \n", | ||
" tf.keras.layers.Dense(10, activation='sigmoid')\n", | ||
"])" | ||
], | ||
"execution_count": 3, | ||
"outputs": [] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"id": "hQyugpgRIyrA" | ||
}, | ||
"source": [ | ||
"Choosing our loss function, in this case crossentropy:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"metadata": { | ||
"id": "RSkzdv8MD0tT" | ||
}, | ||
"source": [ | ||
"loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)" | ||
], | ||
"execution_count": 4, | ||
"outputs": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"metadata": { | ||
"id": "9foNKHzTD2Vo" | ||
}, | ||
"source": [ | ||
"model.compile(optimizer='adam',\n", | ||
" loss=loss_fn,\n", | ||
" metrics=['accuracy'])" | ||
], | ||
"execution_count": 5, | ||
"outputs": [] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"id": "ix4mEL65on-w" | ||
}, | ||
"source": [ | ||
"Adjusting the model parameters to minimize the loss of our model: " | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"metadata": { | ||
"id": "y7suUbJXVLqP", | ||
"outputId": "679a2a7d-90f2-4827-deb6-b950793b227e", | ||
"colab": { | ||
"base_uri": "https://localhost:8080/", | ||
"height": 217 | ||
} | ||
}, | ||
"source": [ | ||
"model.fit(x_train, y_train, epochs=5)" | ||
], | ||
"execution_count": 6, | ||
"outputs": [ | ||
{ | ||
"output_type": "stream", | ||
"text": [ | ||
"Epoch 1/5\n", | ||
"1875/1875 [==============================] - 5s 2ms/step - loss: 1.5402 - accuracy: 0.9171\n", | ||
"Epoch 2/5\n", | ||
"1875/1875 [==============================] - 4s 2ms/step - loss: 1.4864 - accuracy: 0.9688\n", | ||
"Epoch 3/5\n", | ||
"1875/1875 [==============================] - 5s 2ms/step - loss: 1.4800 - accuracy: 0.9761\n", | ||
"Epoch 4/5\n", | ||
"1875/1875 [==============================] - 5s 2ms/step - loss: 1.4767 - accuracy: 0.9798\n", | ||
"Epoch 5/5\n", | ||
"1875/1875 [==============================] - 4s 2ms/step - loss: 1.4751 - accuracy: 0.9813\n" | ||
], | ||
"name": "stdout" | ||
}, | ||
{ | ||
"output_type": "execute_result", | ||
"data": { | ||
"text/plain": [ | ||
"<tensorflow.python.keras.callbacks.History at 0x7f1ec03ca630>" | ||
] | ||
}, | ||
"metadata": { | ||
"tags": [] | ||
}, | ||
"execution_count": 6 | ||
} | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"id": "4mDAAPFqVVgn" | ||
}, | ||
"source": [ | ||
"Checking the model's performance on the Validation-set." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"metadata": { | ||
"id": "F7dTAzgHDUh7", | ||
"outputId": "8cc7a2b0-5cbd-49f1-cd9d-544a0d4e2e50", | ||
"colab": { | ||
"base_uri": "https://localhost:8080/", | ||
"height": 54 | ||
} | ||
}, | ||
"source": [ | ||
"model.evaluate(x_test, y_test, verbose=2)" | ||
], | ||
"execution_count": 7, | ||
"outputs": [ | ||
{ | ||
"output_type": "stream", | ||
"text": [ | ||
"313/313 - 1s - loss: 1.4754 - accuracy: 0.9819\n" | ||
], | ||
"name": "stdout" | ||
}, | ||
{ | ||
"output_type": "execute_result", | ||
"data": { | ||
"text/plain": [ | ||
"[1.4754043817520142, 0.9818999767303467]" | ||
] | ||
}, | ||
"metadata": { | ||
"tags": [] | ||
}, | ||
"execution_count": 7 | ||
} | ||
] | ||
} | ||
] | ||
} |