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improve notebook display
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‎examples/3 - Neural Networks/convolutional_network.py‎

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'''
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A Multilayer Perceptron implementation example using TensorFlow library.
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A Convolutional Network implementation example using TensorFlow library.
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This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)
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Author: Aymeric Damien

‎notebooks/1 - Introduction/basic_operations.ipynb‎

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},
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"outputs": [],
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"source": [
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"#Basic Operations example using TensorFlow library.\n",
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"#Author: Aymeric Damien\n",
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"#Project: https://github.com/aymericdamien/TensorFlow-Examples/"
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"# Basic Operations example using TensorFlow library.\n",
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"# Author: Aymeric Damien\n",
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"# Project: https://github.com/aymericdamien/TensorFlow-Examples/"
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]
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},
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{
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"# The output of the op is returned in 'result' as a numpy `ndarray` object.\n",
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"with tf.Session() as sess:\n",
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" result = sess.run(product)\n",
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" print result\n",
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" # ==> [[ 12.]]"
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" print result"
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]
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}
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],

‎notebooks/1 - Introduction/helloworld.ipynb‎

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},
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"outputs": [],
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"source": [
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"#Simple hello world using TensorFlow\n",
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"# Simple hello world using TensorFlow\n",
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"\n",
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"# Create a Constant op\n",
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"# The op is added as a node to the default graph.\n",

‎notebooks/2 - Basic Classifiers/linear_regression.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 22,
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"metadata": {
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"collapsed": false
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},
@@ -189,11 +189,13 @@
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"\n",
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" #Display logs per epoch step\n",
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" if epoch % display_step == 0:\n",
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" print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \\\n",
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" print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n",
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" \"{:.9f}\".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \\\n",
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" \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
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"\n",
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" print \"Optimization Finished!\"\n",
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" print \"cost=\", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
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" print \"cost=\", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \\\n",
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" \"W=\", sess.run(W), \"b=\", sess.run(b)\n",
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"\n",
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" #Graphic display\n",
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" plt.plot(train_X, train_Y, 'ro', label='Original data')\n",
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 23,
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"metadata": {
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"collapsed": false
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},
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 18,
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}

‎notebooks/2 - Basic Classifiers/logistic_regression.ipynb‎

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"outputs": [],
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"source": [
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"# A logistic regression learning algorithm example using TensorFlow library.\n",
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"# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n",
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"# This example is using the MNIST database of handwritten digits \n",
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"# (http://yann.lecun.com/exdb/mnist/)\n",
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"\n",
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"# Author: Aymeric Damien\n",
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"# Project: https://github.com/aymericdamien/TensorFlow-Examples/"
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"outputs": [],
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"source": [
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"# Minimize error using cross entropy\n",
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"cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy\n",
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"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent"
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"# Cross entropy\n",
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"cost = -tf.reduce_sum(y*tf.log(activation)) \n",
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"# Gradient Descent\n",
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"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) "
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]
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},
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{

‎notebooks/2 - Basic Classifiers/nearest_neighbor.ipynb‎

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"outputs": [],
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"source": [
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"# A nearest neighbor learning algorithm example using TensorFlow library.\n",
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"# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n",
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"# This example is using the MNIST database of handwritten digits \n",
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"# (http://yann.lecun.com/exdb/mnist/)\n",
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"\n",
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"# Author: Aymeric Damien\n",
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"# Project: https://github.com/aymericdamien/TensorFlow-Examples/"
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" # Get nearest neighbor\n",
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" nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i,:]})\n",
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" # Get nearest neighbor class label and compare it to its true label\n",
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" print \"Test\", i, \"Prediction:\", np.argmax(Ytr[nn_index]), \"True Class:\", np.argmax(Yte[i])\n",
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" print \"Test\", i, \"Prediction:\", np.argmax(Ytr[nn_index]), \\\n",
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" \"True Class:\", np.argmax(Yte[i])\n",
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" # Calculate accuracy\n",
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" if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):\n",
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" accuracy += 1./len(Xte)\n",
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" print \"Done!\"\n",
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" print \"Accuracy:\", accuracy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎notebooks/3 - Neural Networks/convolutional_network.ipynb‎

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},
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"outputs": [],
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"source": [
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"# A Multilayer Perceptron implementation example using TensorFlow library.\n",
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"# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n",
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"# A Convolutional Networ implementation example using TensorFlow library.\n",
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"# This example is using the MNIST database of handwritten digits\n",
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"# (http://yann.lecun.com/exdb/mnist/)\n",
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"\n",
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"# Author: Aymeric Damien\n",
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"# Project: https://github.com/aymericdamien/TensorFlow-Examples/"
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"source": [
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"# Create model\n",
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"def conv2d(img, w, b):\n",
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" return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], padding='SAME'),b))\n",
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" return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], \n",
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" padding='SAME'),b))\n",
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"\n",
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"def max_pool(img, k):\n",
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" return tf.nn.max_pool(img, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')\n",
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"source": [
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"# Store layers weight & bias\n",
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"weights = {\n",
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" 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), # 5x5 conv, 1 input, 32 outputs\n",
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" 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), # 5x5 conv, 32 inputs, 64 outputs\n",
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" 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), # fully connected, 7*7*64 inputs, 1024 outputs\n",
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" 'out': tf.Variable(tf.random_normal([1024, n_classes])) # 1024 inputs, 10 outputs (class prediction)\n",
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" # 5x5 conv, 1 input, 32 outputs\n",
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" 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), \n",
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" # 5x5 conv, 32 inputs, 64 outputs\n",
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" 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), \n",
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" # fully connected, 7*7*64 inputs, 1024 outputs\n",
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" 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), \n",
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" # 1024 inputs, 10 outputs (class prediction)\n",
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" 'out': tf.Variable(tf.random_normal([1024, n_classes])) \n",
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"}\n",
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"\n",
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"biases = {\n",

‎notebooks/3 - Neural Networks/multilayer_perceptron.ipynb‎

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"outputs": [],
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"source": [
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"# A Multilayer Perceptron implementation example using TensorFlow library.\n",
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"# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n",
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"# This example is using the MNIST database of handwritten digits\n",
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"# (http://yann.lecun.com/exdb/mnist/)\n",
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"\n",
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"# Author: Aymeric Damien\n",
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"# Project: https://github.com/aymericdamien/TensorFlow-Examples/"
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"source": [
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"# Create model\n",
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"def multilayer_perceptron(_X, _weights, _biases):\n",
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" layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) #Hidden layer with RELU activation\n",
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" layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) #Hidden layer with RELU activation\n",
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" #Hidden layer with RELU activation\n",
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" layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) \n",
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" #Hidden layer with RELU activation\n",
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" layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) \n",
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" return tf.matmul(layer_2, weights['out']) + biases['out']"
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]
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},
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"outputs": [],
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"source": [
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"# Define loss and optimizer\n",
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"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss\n",
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"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer"
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"# Softmax loss\n",
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"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) \n",
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"# Adam Optimizer\n",
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"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) "
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]
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},
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{

‎tensorflow_setup.md‎

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