def main(args):
# Import data
mnist = input_data.read_data_sets(args.data_dir, one_hot=True)
model = Model(layers=[
Dense(10,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
input_shape=(784, ))
],
optimizer=GradientDescent(learning_rate=0.5),
loss=SoftMaxCrossEntropyWithLogits())
# Train
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
model.fit_batch(batch_xs, batch_ys)
# Test trained model
actual_labels = np.argmax(mnist.test.labels, 1)
predictions = model.predict(mnist.test.images)
predicted_labels = np.argmax(predictions, 1)
accuracy = (actual_labels == predicted_labels).mean()
print("Test accuracy: {}".format(accuracy))
def main(args):
# Import data
mnist = input_data.read_data_sets(args.data_dir, one_hot=True)
relu = ReLU()
model = Model(layers=[
Conv2D(filter_size=(10, 10),
input_shape=(28, 28, 1),
stride=(1, 1),
channels=32,
activation=relu,
padding='same',
filter_initializer=weight_initializer,
bias_initializer=bias_initializer),
MaxPool2D(pool_size=(2, 2)),
Conv2D(filter_size=(5, 5),
stride=(1, 1),
channels=16,
activation=relu,
padding='same',
filter_initializer=weight_initializer,
bias_initializer=bias_initializer),
MaxPool2D(pool_size=(2, 2)),
Flatten(),
Dense(1024,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
activation=relu),
Dense(10,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer)
],
optimizer=AdaGrad(learning_rate=0.001, epsilon=1e-8),
loss=SoftMaxCrossEntropyWithLogits())
# Train
for _ in range(200):
batch_xs, batch_ys = mnist.train.next_batch(100)
batch_xs = np.reshape(batch_xs, [-1, 28, 28, 1])
model.fit_batch(batch_xs, batch_ys)
if args.verbose:
print('Batch {} loss: {}'.format(
model.batch_number,
model.loss.compute(model.predict(batch_xs), batch_ys)))
# Test trained model
actual_labels = np.argmax(mnist.test.labels, 1)
predictions = model.predict(np.reshape(mnist.test.images, [-1, 28, 28, 1]))
predicted_labels = np.argmax(predictions, 1)
accuracy = (actual_labels == predicted_labels).mean()
print("Test accuracy: {}".format(accuracy))