def train(sess, mnist, n_training_epochs, batch_size, summaries_op,
accuracy_summary_op, train_writer, test_writer, X, Y, train_op,
loss_op, accuracy_op):
# compute number of batches for given batch_size
num_train_batches = mnist.train.num_examples // batch_size
# record starting time
train_start = time.time()
# Run through the entire dataset n_training_epochs times
for i in range(n_training_epochs):
# Initialise statistics
training_loss = 0
epoch_start = time.time()
# Run the SGD train op for each minibatch
for _ in range(num_train_batches):
batch = mnist.train.next_batch(batch_size)
trainstep_result, batch_loss, summary = \
qfns.train_step(sess, batch, X, Y, train_op, loss_op, summaries_op)
train_writer.add_summary(summary, i)
training_loss += batch_loss
# Timing and statistics
epoch_duration = round(time.time() - epoch_start, 2)
ave_train_loss = training_loss / num_train_batches
# Get accuracy
train_accuracy = \
accuracy(sess, mnist.train, batch_size, X, Y, accuracy_op)
test_accuracy = \
accuracy(sess, mnist.test, batch_size, X, Y, accuracy_op)
# log accuracy at the current epoch on training and test sets
train_acc_summary = sess.run(
accuracy_summary_op,
feed_dict={accuracy_placeholder: train_accuracy})
train_writer.add_summary(train_acc_summary, i)
test_acc_summary = sess.run(
accuracy_summary_op,
feed_dict={accuracy_placeholder: test_accuracy})
test_writer.add_summary(test_acc_summary, i)
[writer.flush() for writer in [train_writer, test_writer]]
train_duration = round(time.time() - train_start, 2)
# Output to montior training
print('Epoch {0}, Training Loss: {1}, Test accuracy: {2}, \
time: {3}s, total time: {4}s'.format(i, ave_train_loss, test_accuracy,
epoch_duration, train_duration))
print('Total training time: {0}s'.format(train_duration))
print('Confusion Matrix:')
true_class = tf.argmax(Y, 1)
predicted_class = tf.argmax(preds_op, 1)
cm = tf.confusion_matrix(predicted_class, true_class)
print(sess.run(cm, feed_dict={X: mnist.test.images, Y: mnist.test.labels}))
def train(sess, mnist, n_training_epochs, batch_size,
summaries_op, accuracy_summary_op, train_writer, test_writer,
X, Y, train_op, loss_op, accuracy_op):
# compute number of batches for given batch_size
num_train_batches = mnist.train.num_examples // batch_size
# record starting time
train_start = time.time()
# Run through the entire dataset n_training_epochs times
for i in range(n_training_epochs):
# Initialise statistics
training_loss = 0
epoch_start = time.time()
# Run the SGD train op for each minibatch
for _ in range(num_train_batches):
batch = mnist.train.next_batch(batch_size)
trainstep_result, batch_loss, summary = \
qfns.train_step(sess, batch, X, Y, train_op, loss_op, summaries_op)
train_writer.add_summary(summary, i)
training_loss += batch_loss
# Timing and statistics
epoch_duration = round(time.time() - epoch_start, 2)
ave_train_loss = training_loss / num_train_batches
# Get accuracy
train_accuracy = \
accuracy(sess, mnist.train, batch_size, X, Y, accuracy_op)
test_accuracy = \
accuracy(sess, mnist.test, batch_size, X, Y, accuracy_op)
# log accuracy at the current epoch on training and test sets
train_acc_summary = sess.run(accuracy_summary_op,
feed_dict={accuracy_placeholder: train_accuracy})
train_writer.add_summary(train_acc_summary, i)
test_acc_summary = sess.run(accuracy_summary_op,
feed_dict={accuracy_placeholder: test_accuracy})
test_writer.add_summary(test_acc_summary, i)
[writer.flush() for writer in [train_writer, test_writer]]
train_duration = round(time.time() - train_start, 2)
# Output to montior training
print('Epoch {0}, Training Loss: {1}, Test accuracy: {2}, \
time: {3}s, total time: {4}s'.format(i, ave_train_loss,
test_accuracy, epoch_duration,
train_duration))
print('Total training time: {0}s'.format(train_duration))
print('Confusion Matrix:')
true_class=tf.argmax(Y, 1)
predicted_class=tf.argmax(preds_op, 1)
cm=tf.confusion_matrix(predicted_class,true_class)
print(sess.run(cm, feed_dict={X: mnist.test.images,
Y: mnist.test.labels}))
def train(sess,
mnist,
n_training_epochs,
batch_size,
X,
Y,
train_op,
loss_op,
accuracy_op,
accuracy_placeholder,
summaries_op=None,
accuracy_summary_op=None,
train_writer=None,
test_writer=None):
# compute number of batches for given batch_size
num_train_batches = mnist.train.num_examples // batch_size
# record starting time
train_start = time.time()
# Run through the entire dataset n_training_epochs times
for i in range(n_training_epochs):
# Initialise statistics
training_loss = 0
epoch_start = time.time()
# Run the SGD train op for each minibatch
for _ in range(num_train_batches):
batch = mnist.train.next_batch(batch_size)
# trainstep_result, batch_loss, summary = \
# qfns.train_step(sess, batch, X, Y, train_op, loss_op, summaries_op)
trainstep_result, batch_loss, _ = \
qfns.train_step(sess, batch, X, Y, train_op, loss_op)
# train_writer.add_summary(summary, i)
training_loss += batch_loss
# Timing and statistics
epoch_duration = round(time.time() - epoch_start, 2)
ave_train_loss = training_loss / num_train_batches
# Get accuracy
train_accuracy = \
accuracy(sess, mnist.train, batch_size, X, Y, accuracy_op, mnist)
test_accuracy = \
accuracy(sess, mnist.test, batch_size, X, Y, accuracy_op, mnist)
# log accuracy at the current epoch on training and test sets
# train_acc_summary = sess.run(accuracy_summary_op,
# feed_dict={accuracy_placeholder: train_accuracy})
# train_writer.add_summary(train_acc_summary, i)
# test_acc_summary = sess.run(accuracy_summary_op,
# feed_dict={accuracy_placeholder: test_accuracy})
# test_writer.add_summary(test_acc_summary, i)
# [writer.flush() for writer in [train_writer, test_writer]]
train_duration = round(time.time() - train_start, 2)
# Output to montior training
print('Epoch {0}, Training Loss: {1}, Test accuracy: {2}, \
time: {3}s, total time: {4}s'.format(i, ave_train_loss,
test_accuracy,
epoch_duration,
train_duration))
print('Total training time: {0}s'.format(train_duration))
return train_accuracy, test_accuracy
