# TRAINS - Example of tensorflow mnist training model logging # # Save and Restore a model using TensorFlow. # 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 from os.path import exists import numpy as np import tensorflow as tf from trains import Task MODEL_PATH = "/tmp/module_no_signatures" task = Task.init(project_name='examples', task_name='Tensorflow mnist example') ## block X_train = np.random.rand(100, 3) y_train = np.random.rand(100, 1) model = tf.keras.models.Sequential([tf.keras.layers.Dense(1)]) model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.SGD(), metrics=['accuracy']) model.fit(X_train, y_train, steps_per_epoch=1, nb_epoch=1) with tf.Session(graph=tf.Graph()) as sess: if exists(MODEL_PATH): try: tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], MODEL_PATH) m2 = tf.saved_model.load(sess, [tf.saved_model.tag_constants.SERVING], MODEL_PATH) except Exception: pass tf.train.Checkpoint ## block end # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # Parameters parameters = { 'learning_rate': 0.001, 'batch_size': 100, 'display_step': 1, 'model_path': "/tmp/model.ckpt", # Network Parameters 'n_hidden_1': 256, # 1st layer number of features 'n_hidden_2': 256, # 2nd layer number of features 'n_input': 784, # MNIST data input (img shape: 28*28) 'n_classes': 10, # MNIST total classes (0-9 digits) } # TRAINS: connect parameters with the experiment/task for logging parameters = task.connect(parameters) # tf Graph input x = tf.placeholder("float", [None, parameters['n_input']]) y = tf.placeholder("float", [None, parameters['n_classes']]) # Create model def multilayer_perceptron(x, weights, biases): # Hidden layer with RELU activation layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) layer_1 = tf.nn.relu(layer_1) # Hidden layer with RELU activation layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) layer_2 = tf.nn.relu(layer_2) # Output layer with linear activation out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] return out_layer # Store layers weight & bias weights = { 'h1': tf.Variable(tf.random_normal([parameters['n_input'], parameters['n_hidden_1']])), 'h2': tf.Variable(tf.random_normal([parameters['n_hidden_1'], parameters['n_hidden_2']])), 'out': tf.Variable(tf.random_normal([parameters['n_hidden_2'], parameters['n_classes']])) } biases = { 'b1': tf.Variable(tf.random_normal([parameters['n_hidden_1']])), 'b2': tf.Variable(tf.random_normal([parameters['n_hidden_2']])), 'out': tf.Variable(tf.random_normal([parameters['n_classes']])) } # Construct model pred = multilayer_perceptron(x, weights, biases) # 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=parameters['learning_rate']).minimize(cost) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # 'Saver' op to save and restore all the variables saver = tf.train.Saver() # Running first session print("Starting 1st session...") with tf.Session() as sess: # Run the initializer sess.run(init) # Training cycle for epoch in range(3): avg_cost = 0. total_batch = int(mnist.train.num_examples/parameters['batch_size']) # Loop over all batches for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(parameters['batch_size']) # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if epoch % parameters['display_step'] == 0: print("Epoch:", '%04d' % (epoch+1), "cost=", \ "{:.9f}".format(avg_cost)) save_path = saver.save(sess, parameters['model_path']) print("First Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) # Save model weights to disk save_path = saver.save(sess, parameters['model_path']) print("Model saved in file: %s" % save_path) # Running a new session print("Starting 2nd session...") with tf.Session() as sess: # Initialize variables sess.run(init) # Restore model weights from previously saved model saver.restore(sess, parameters['model_path']) print("Model restored from file: %s" % save_path) # Resume training for epoch in range(7): avg_cost = 0. total_batch = int(mnist.train.num_examples / parameters['batch_size']) # Loop over all batches for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(parameters['batch_size']) # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if epoch % parameters['display_step'] == 0: print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost)) print("Second Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print("Accuracy:", accuracy.eval( {x: mnist.test.images, y: mnist.test.labels}))