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‎Chapter01/setupDeepLearning.sh

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#!/bin/bash
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# Save this script to /home/yourUserName, chmod +x setupDeepLearning.sh, run using ./setupDeepLearning.sh
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mkdir tensorflow
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cd tensorflow
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################################################################################
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# Install utils.
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################################################################################
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echo -e "\e[36m***Installing utilities*** \e[0m"
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sudo apt-get update
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sudo apt-get install unzip git-all pkg-config zip g++ zlib1g-dev
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################################################################################
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# Install Java deps.
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################################################################################
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echo -e "\e[36m***Installing Java8. Press ENTER when prompted*** \e[0m"
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echo -e "\e[36m***And accept licence*** \e[0m"
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sudo add-apt-repository ppa:webupd8team/java
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sudo apt-get update
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sudo apt-get install oracle-java8-installer
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################################################################################
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# Install Bazel dep.
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################################################################################
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echo -e "\e[36m***Installing Bazel*** \e[0m"
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wget https://github.com/bazelbuild/bazel/releases/download/0.11.1/bazel-0.11.1-without-jdk-installer-linux-x86_64.sh -O bazel-installer-linux-x86_64.sh
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chmod +x bazel-installer-linux-x86_64.sh
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sudo ./bazel-installer-linux-x86_64.sh
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rm bazel-installer-linux-x86_64.sh
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sudo chown $USER:$USER ~/.cache/bazel/
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################################################################################
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# Fetch Swig and Python deps.
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################################################################################
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echo -e "\e[36m***Installing python deps*** \e[0m"
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sudo apt-get install swig
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sudo apt-get install build-essential python-dev python-pip checkinstall
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sudo apt-get install libreadline-gplv2-dev libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev libbz2-dev
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################################################################################
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# Fetch and install Python.
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################################################################################
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echo -e "\e[36m***Installing Python*** \e[0m"
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wget https://www.python.org/ftp/python/2.7.14/Python-2.7.14.tgz
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tar -xvf Python-2.7.14.tgz
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cd Python-2.7.14
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./configure
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make
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sudo make install
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cd ../
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rm Python-2.7.14.tgz
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################################################################################
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# Grab TensorFlow CPU version from central repo
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################################################################################
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echo -e "\e[36m***Cloning TensorFlow from GitHub*** \e[0m"
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sudo pip install tensorflow
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################################################################################
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# Grab Keras from repo
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################################################################################
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echo -e "\e[36m***Installing Keras with Tensorflow backend*** \e[0m"
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sudo pip install keras
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################################################################################
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# Installing other python dependencies
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################################################################################
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echo -e "\e[36m***Installing Python Deps*** \e[0m"
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sudo apt-get install python-numpy
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sudo pip install numpy --upgrade
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sudo pip --no-cache-dir install Pillow pandas scipy sklearn
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sudo pip install web.py gunicorn
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echo -e "\e[36mReady to run TensorFlow! \e[0m"
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################################################################################
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# Checking the installations
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################################################################################
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echo -e "\e[36m***Listing modules*** \e[0m"
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pip list

‎Chapter02/.DS_Store

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‎Chapter02/hy_param.py

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#!/usr/bin/env python2
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# Hyperparameters and all other kind of params
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# Parameters
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learning_rate = 0.01
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num_steps = 100
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batch_size = 128
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display_step = 1
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# Network Parameters
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n_hidden_1 = 300 # 1st layer number of neurons
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n_hidden_2 = 300 # 2nd layer number of neurons
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num_input = 784 # MNIST data input (img shape: 28*28)
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num_classes = 10 # MNIST total classes (0-9 digits)
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#Training Parameters
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checkpoint_every = 100
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checkpoint_dir = './runs/'

‎Chapter02/inference.py

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#!/usr/bin/env python2
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# -*- coding: utf-8 -*-
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from __future__ import print_function
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import os
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import numpy as np
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import tensorflow as tf
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import matplotlib.pyplot as plt
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import hy_param
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from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
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# Pointing the model checkpoint
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checkpoint_file = tf.train.latest_checkpoint(os.path.join(hy_param.checkpoint_dir, 'checkpoints'))
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saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
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# Loading test data
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test_data = np.array([mnist.test.images[6]])
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# Loading input variable from the model
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input_x = tf.get_default_graph().get_operation_by_name("input_x").outputs[0]
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# Loading Prediction operation
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prediction = tf.get_default_graph().get_operation_by_name("prediction").outputs[0]
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with tf.Session() as sess:
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# Restoring the model from the checkpoint
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saver.restore(sess, checkpoint_file)
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# Executing the model to make predictions
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data = sess.run(prediction, feed_dict={input_x: test_data })
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print("Predicted digit: ", data.argmax() )
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# Display the feed image
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print ("Input image:")
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plt.gray()
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plt.imshow(test_data.reshape([28,28]))

‎Chapter02/model.py

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#!/usr/bin/env python2
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# -*- coding: utf-8 -*-
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import tensorflow as tf
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import hy_param
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## Defining Placeholders which will be used as inputs for the model
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X = tf.placeholder("float", [None, hy_param.num_input],name="input_x")
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Y = tf.placeholder("float", [None, hy_param.num_classes],name="input_y")
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# Defining variables for weights & bias
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weights = {
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'h1': tf.Variable(tf.random_normal([hy_param.num_input, hy_param.n_hidden_1])),
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'h2': tf.Variable(tf.random_normal([hy_param.n_hidden_1, hy_param.n_hidden_2])),
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'out': tf.Variable(tf.random_normal([hy_param.n_hidden_2, hy_param.num_classes]))
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}
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biases = {
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'b1': tf.Variable(tf.random_normal([hy_param.n_hidden_1])),
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'b2': tf.Variable(tf.random_normal([hy_param.n_hidden_2])),
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'out': tf.Variable(tf.random_normal([hy_param.num_classes]))
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}
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# Hidden fully connected layer 1 with 300 neurons
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layer_1 = tf.add(tf.matmul(X, weights['h1']), biases['b1'])
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# Hidden fully connected layer 2 with 300 neurons
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layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
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# Output fully connected layer with a neuron for each class
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logits = tf.matmul(layer_2, weights['out']) + biases['out']
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# Performing softmax operation
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prediction = tf.nn.softmax(logits, name='prediction')
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# Define loss and optimizer
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loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
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logits=logits, labels=Y))
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optimizer = tf.train.AdamOptimizer(learning_rate=hy_param.learning_rate)
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train_op = optimizer.minimize(loss_op)
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# Evaluate model
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correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
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accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32) ,name='accuracy')

‎Chapter02/train.py

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#!/usr/bin/env python2
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# -*- coding: utf-8 -*-
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from __future__ import print_function
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# Import MNIST data
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import os
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from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
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import tensorflow as tf
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import model
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import hy_param
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## tf Graph input
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X = model.X
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Y = model.Y
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checkpoint_dir = os.path.abspath(os.path.join(hy_param.checkpoint_dir, "checkpoints"))
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checkpoint_prefix = os.path.join(checkpoint_dir, "model")
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if not os.path.exists(checkpoint_dir):
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os.makedirs(checkpoint_dir)
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saver = tf.train.Saver(tf.global_variables(), max_to_keep=2)
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#loss = tf.Variable(0.0)
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# Initialize the variables
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init = tf.global_variables_initializer()
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all_loss = []
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# Start training
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with tf.Session() as sess:
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writer_1 = tf.summary.FileWriter("./runs/summary/",sess.graph)
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sum_var = tf.summary.scalar("loss", model.accuracy)
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write_op = tf.summary.merge_all()
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# Run the initializer
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sess.run(init)
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for step in range(1, hy_param.num_steps+1):
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# Extracting
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batch_x, batch_y = mnist.train.next_batch(hy_param.batch_size)
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# Run optimization op (backprop)
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sess.run(model.train_op, feed_dict={X: batch_x, Y: batch_y})
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if step % hy_param.display_step == 0 or step == 1:
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# Calculate batch loss and accuracy
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loss, acc, summary = sess.run([model.loss_op, model.accuracy, write_op], feed_dict={X: batch_x,
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Y: batch_y})
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all_loss.append(loss)
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writer_1.add_summary(summary, step)
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print("Step " + str(step) + ", Minibatch Loss= " + \
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"{:.4f}".format(loss) + ", Training Accuracy= " + \
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"{:.3f}".format(acc))
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if step % hy_param.checkpoint_every == 0:
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path = saver.save(
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sess, checkpoint_prefix, global_step=step)
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# print("Saved model checkpoint to {}\n".format(path))
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print("Optimization Finished!")
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# Calculate accuracy for MNIST test images
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print("Testing Accuracy:", \
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sess.run(model.accuracy, feed_dict={X: mnist.test.images,
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Y: mnist.test.labels}))
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‎Chapter03/.DS_Store

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