# floydhub/quick-start

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 ''' A Convolutional Network implementation example using TensorFlow library. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' from __future__ import print_function import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/mnist", one_hot=True) # Parameters learning_rate = 0.001 training_iters = 200000 batch_size = 128 display_step = 10 # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) dropout = 0.75 # Dropout, probability to keep units # tf Graph input x = tf.placeholder(tf.float32, [None, n_input], name="x") y = tf.placeholder(tf.float32, [None, n_classes], name="y") keep_prob = tf.placeholder(tf.float32, name="keep_prob") #dropout (keep probability) # Create some wrappers for simplicity def conv2d(x, W, b, strides=1): # Conv2D wrapper, with bias and relu activation x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') x = tf.nn.bias_add(x, b) return tf.nn.relu(x) def maxpool2d(x, k=2): # MaxPool2D wrapper return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME') # Create model def conv_net(x, weights, biases, dropout): # Reshape input picture x = tf.reshape(x, shape=[-1, 28, 28, 1]) # Convolution Layer conv1 = conv2d(x, weights['wc1'], biases['bc1']) # Max Pooling (down-sampling) conv1 = maxpool2d(conv1, k=2) # Convolution Layer conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) # Max Pooling (down-sampling) conv2 = maxpool2d(conv2, k=2) # Fully connected layer # Reshape conv2 output to fit fully connected layer input fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) fc1 = tf.nn.relu(fc1) # Apply Dropout fc1 = tf.nn.dropout(fc1, dropout) # Output, class prediction out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) return out # Store layers weight & bias weights = { # 5x5 conv, 1 input, 32 outputs 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), # 5x5 conv, 32 inputs, 64 outputs 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), # fully connected, 7*7*64 inputs, 1024 outputs 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), # 1024 inputs, 10 outputs (class prediction) 'out': tf.Variable(tf.random_normal([1024, n_classes])) } biases = { 'bc1': tf.Variable(tf.random_normal([32])), 'bc2': tf.Variable(tf.random_normal([64])), 'bd1': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes])) } # Construct model pred = conv_net(x, weights, biases, keep_prob) # Define loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name="accuracy") # Initializing the variables init = tf.global_variables_initializer() builder = tf.saved_model.builder.SavedModelBuilder("cnn_model") # Launch the graph with tf.Session() as sess: sess.run(init) step = 1 # Keep training until reach max iterations while step * batch_size < training_iters: batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout}) if step % display_step == 0: # Calculate batch loss and accuracy loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.}) print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc)) step += 1 print("Optimization Finished!") builder.add_meta_graph_and_variables(sess, ["EVALUATING"]) builder.save()