import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data LOCAL_FOLDER = "MNIST_data/" IMAGE_PIXELS = 784 NUM_CLASSES = 10 HIDDEN1_UNITS = 500 HIDDEN2_UNITS = 300 LEARNING_RATE = 1e-4 TRAINING_STEPS = 2000 BATCH_SIZE = 100 def dense_layer(x, in_dim, out_dim, layer_name, act): """Creates a single densely connected layer of a NN""" with tf.name_scope(layer_name): # layer weights corresponding to the input / output dimensions weights = tf.Variable( tf.truncated_normal( [in_dim, out_dim], stddev=1.0 / tf.sqrt(float(out_dim)) ), name="weights" ) # layer biases corresponding to output dimension biases = tf.Variable(tf.zeros([out_dim]), name="biases") # layer activations applied to Wx+b layer = act(tf.matmul(x, weights) + biases, name="activations") return layer # PREPARING DATA # downloading (on first run) and extracting MNIST data data = input_data.read_data_sets(LOCAL_FOLDER, one_hot=True, validation_size=0) # BUILDING COMPUTATIONAL GRAPH # model inputs: input pixels and targets input = tf.placeholder(tf.float32, [None, IMAGE_PIXELS], name="input") targets = tf.placeholder(tf.float32, [None, NUM_CLASSES], name="targets") # network layers: two hidden and one output hidden1 = dense_layer(input, IMAGE_PIXELS, HIDDEN1_UNITS, "hidden1", act=tf.nn.relu) hidden2 = dense_layer(hidden1, HIDDEN1_UNITS, HIDDEN2_UNITS, "hidden2", act=tf.nn.relu) output = dense_layer(hidden2, HIDDEN2_UNITS, NUM_CLASSES, "output", act=tf.identity) # loss function: cross-entropy with built-in # (stable) computation of softmax from logits cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( labels=targets, logits=output ) ) # training algorithm: Adam with configurable learning rate train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(cross_entropy) # evaluation operation: ratio of correct predictions correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(targets, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # RUNNING COMPUTATIONAL GRAPH # creating session sess = tf.InteractiveSession() # initializing trainable variables sess.run(tf.global_variables_initializer()) # training loop for step in range(TRAINING_STEPS): # fetching next batch of training data batch_xs, batch_ys = data.train.next_batch(BATCH_SIZE) if step % 100 == 0: # reporting current accuracy of the model on every 100th batch batch_accuracy = sess.run(accuracy, feed_dict={input: batch_xs, targets: batch_ys}) print("{0}:\tbatch accuracy {1:.2f}".format(step, batch_accuracy)) # running the training step with the fetched batch sess.run(train_step, feed_dict={input: batch_xs, targets: batch_ys}) # evaluating model prediction accuracy of the model on the test set test_accuracy = sess.run(accuracy, feed_dict={input: data.test.images, targets: data.test.labels}) print("-------------------------------------------------") print("Test set accuracy: {0:.4f}".format(test_accuracy))