# Calculate accuracy correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # TODO: Set batch size batch_size = 128 assert batch_size is not None, 'You must set the batch size' init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # TODO: Train optimizer on all batches # for batch_features, batch_labels in ____ for batch_features, batch_labels in batches(batch_size, train_features, train_labels): sess.run(optimizer, feed_dict={ features: batch_features, labels: batch_labels }) # Calculate accuracy for test dataset test_accuracy = sess.run(accuracy, feed_dict={ features: test_features, labels: test_labels }) print('Test Accuracy: {}'.format(test_accuracy))
# Define loss and optimizer learning_rate = tf.placeholder(tf.float32) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) # Calculate accuracy correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) init = tf.global_variables_initializer() batch_size = 128 epochs = 10 learn_rate = 0.001 train_batches = batches(batch_size, train_features, train_labels) with tf.Session() as sess: sess.run(init) # Training cycle for epoch_i in range(epochs): # Loop over all batches for batch_features, batch_labels in train_batches: train_feed_dict = { features: batch_features, labels: batch_labels, learning_rate: learn_rate} sess.run(optimizer, feed_dict=train_feed_dict)
tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)) optimizer = tf.train.GradientDescentOptimizer( learning_rate=learning_rate).minimize(cost) correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) batch_size = 128 assert batch_size is not None init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for batch_features, batch_lables in batches(batch_size, train_features, train_features): sess.run(optimizer, feed_dict={ features: batch_features, labels: batch_lables }) test_accuracy = sess.run(accuracy, feed_dict={ features: batch_features, labels: batch_lables }) print('Test accuracy: {}'.format(test_accuracy))