# 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))
