def main_withoutCV(_):
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 14])
# Build the graph for the deep net
y_conv, keep_prob = deepnn(x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location = tempfile.mkdtemp()
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
with tf.Session() as sess:
j = 0
max_train_accuracy = 0
sess.run(tf.global_variables_initializer())
(X_train, Y_train) = preprocess.training_set(crop=False,
rotate_e=True) # TODO
(X_test, Y_test) = preprocess.test_set()
for i in range(epochs):
for (Xb, Yb) in next_batch(X_train, Y_train, 41):
train_step.run(feed_dict={x: Xb, y_: Yb, keep_prob: 0.5})
if i % 25 == 0: # log
train_accuracy = accuracy.eval(feed_dict={
x: X_train,
y_: Y_train,
keep_prob: 1.0
})
print('step %d, training accuracy %g' % (i, train_accuracy))
if train_accuracy == 1:
break
if train_accuracy > max_train_accuracy:
max_train_accuracy = train_accuracy
j = 0
else:
j += 1
if j == 5:
break
print('test accuracy %g' % accuracy.eval(feed_dict={
x: X_test,
y_: Y_test,
keep_prob: 1.0
}))
def main(_):
cnnData = CnnData()
for r in range(cv_fold):
cnnData.nextCVRound()
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 14])
# Build the graph for the deep net
y_conv, keep_prob, mode = deepnn(x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.AdamOptimizer(1e-4).minimize(
cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1),
tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location = tempfile.mkdtemp()
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(1):
for (Xb, Yb) in next_batch(cnnData.X_train, cnnData.Y_train,
batchSize):
train_step.run(feed_dict={
x: Xb,
y_: Yb,
keep_prob: 0.5,
mode: True
})
if i % 25 == 0: # log
train_accuracy = accuracy.eval(
feed_dict={
x: cnnData.X_train,
y_: cnnData.Y_train,
keep_prob: 1.0,
mode: False
})
print('step %d, training accuracy %g' %
(i, train_accuracy))
if train_accuracy == 1:
break
print('test accuracy %g' % accuracy.eval(
feed_dict={
x: cnnData.X_test,
y_: cnnData.Y_test,
keep_prob: 1.0,
mode: False
}))