def test_conv_layers(self):
X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]]
Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]]
with tf.Graph().as_default():
g = zqtflearn.input_data(shape=[None, 4])
g = zqtflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = zqtflearn.conv_2d(g, 4, 2, activation='relu')
g = zqtflearn.max_pool_2d(g, 2)
g = zqtflearn.fully_connected(g, 2, activation='softmax')
g = zqtflearn.regression(g, optimizer='sgd', learning_rate=1.)
m = zqtflearn.DNN(g)
m.fit(X, Y, n_epoch=100, snapshot_epoch=False)
# TODO: Fix test
#self.assertGreater(m.predict([[1., 0., 0., 0.]])[0][0], 0.5)
# Bulk Tests
with tf.Graph().as_default():
g = zqtflearn.input_data(shape=[None, 4])
g = zqtflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = zqtflearn.conv_2d(g, 4, 2)
g = zqtflearn.conv_2d(g, 4, 1)
g = zqtflearn.conv_2d_transpose(g, 4, 2, [2, 2])
g = zqtflearn.max_pool_2d(g, 2)
def discriminator(x, reuse=False):
with tf.variable_scope('Discriminator', reuse=reuse):
x = zqtflearn.conv_2d(x, 64, 5, activation='tanh')
x = zqtflearn.avg_pool_2d(x, 2)
x = zqtflearn.conv_2d(x, 128, 5, activation='tanh')
x = zqtflearn.avg_pool_2d(x, 2)
x = zqtflearn.fully_connected(x, 1024, activation='tanh')
x = zqtflearn.fully_connected(x, 2)
x = tf.nn.softmax(x)
return x
def generator(x, reuse=False):
with tf.variable_scope('Generator', reuse=reuse):
x = zqtflearn.fully_connected(x, n_units=7 * 7 * 128)
x = zqtflearn.batch_normalization(x)
x = tf.nn.tanh(x)
x = tf.reshape(x, shape=[-1, 7, 7, 128])
x = zqtflearn.upsample_2d(x, 2)
x = zqtflearn.conv_2d(x, 64, 5, activation='tanh')
x = zqtflearn.upsample_2d(x, 2)
x = zqtflearn.conv_2d(x, 1, 5, activation='sigmoid')
return x
def build_dqn(num_actions, action_repeat):
"""
Building a DQN.
"""
inputs = tf.placeholder(tf.float32, [None, action_repeat, 84, 84])
# Inputs shape: [batch, channel, height, width] need to be changed into
# shape [batch, height, width, channel]
net = tf.transpose(inputs, [0, 2, 3, 1])
net = zqtflearn.conv_2d(net, 32, 8, strides=4, activation='relu')
net = zqtflearn.conv_2d(net, 64, 4, strides=2, activation='relu')
net = zqtflearn.fully_connected(net, 256, activation='relu')
q_values = zqtflearn.fully_connected(net, num_actions)
return inputs, q_values
def test_feed_dict_no_None(self):
X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]]
Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]]
with tf.Graph().as_default():
g = zqtflearn.input_data(shape=[None, 4], name="X_in")
g = zqtflearn.reshape(g, new_shape=[-1, 2, 2, 1])
g = zqtflearn.conv_2d(g, 4, 2)
g = zqtflearn.conv_2d(g, 4, 1)
g = zqtflearn.max_pool_2d(g, 2)
g = zqtflearn.fully_connected(g, 2, activation='softmax')
g = zqtflearn.regression(g, optimizer='sgd', learning_rate=1.)
m = zqtflearn.DNN(g)
def do_fit():
m.fit({"X_in": X, 'non_existent': X}, Y, n_epoch=30, snapshot_epoch=False)
self.assertRaisesRegexp(Exception, "Feed dict asks for variable named 'non_existent' but no such variable is known to exist", do_fit)
# Using MNIST Dataset
import zqtflearn.datasets.mnist as mnist
mnist_data = mnist.read_data_sets(one_hot=True)
# User defined placeholders
with tf.Graph().as_default():
# Placeholders for data and labels
X = tf.placeholder(shape=(None, 784), dtype=tf.float32)
Y = tf.placeholder(shape=(None, 10), dtype=tf.float32)
net = tf.reshape(X, [-1, 28, 28, 1])
# Using TFLearn wrappers for network building
net = zqtflearn.conv_2d(net, 32, 3, activation='relu')
net = zqtflearn.max_pool_2d(net, 2)
net = zqtflearn.local_response_normalization(net)
net = zqtflearn.dropout(net, 0.8)
net = zqtflearn.conv_2d(net, 64, 3, activation='relu')
net = zqtflearn.max_pool_2d(net, 2)
net = zqtflearn.local_response_normalization(net)
net = zqtflearn.dropout(net, 0.8)
net = zqtflearn.fully_connected(net, 128, activation='tanh')
net = zqtflearn.dropout(net, 0.8)
net = zqtflearn.fully_connected(net, 256, activation='tanh')
net = zqtflearn.dropout(net, 0.8)
net = zqtflearn.fully_connected(net, 10, activation='linear')
# Defining other ops using Tensorflow
loss = tf.reduce_mean(
def vgg16(input, num_class):
x = zqtflearn.conv_2d(input, 64, 3, activation='relu', scope='conv1_1')
x = zqtflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2')
x = zqtflearn.max_pool_2d(x, 2, strides=2, name='maxpool1')
x = zqtflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1')
x = zqtflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2')
x = zqtflearn.max_pool_2d(x, 2, strides=2, name='maxpool2')
x = zqtflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1')
x = zqtflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2')
x = zqtflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3')
x = zqtflearn.max_pool_2d(x, 2, strides=2, name='maxpool3')
x = zqtflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1')
x = zqtflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2')
x = zqtflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3')
x = zqtflearn.max_pool_2d(x, 2, strides=2, name='maxpool4')
x = zqtflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1')
x = zqtflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2')
x = zqtflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3')
x = zqtflearn.max_pool_2d(x, 2, strides=2, name='maxpool5')
x = zqtflearn.fully_connected(x, 4096, activation='relu', scope='fc6')
x = zqtflearn.dropout(x, 0.5, name='dropout1')
x = zqtflearn.fully_connected(x, 4096, activation='relu', scope='fc7')
x = zqtflearn.dropout(x, 0.5, name='dropout2')
x = zqtflearn.fully_connected(x, num_class, activation='softmax', scope='fc8',
restore=False)
return x
from __future__ import division, print_function, absolute_import
import zqtflearn
import zqtflearn.data_utils as du
# Data loading and preprocessing
import zqtflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
X, mean = du.featurewise_zero_center(X)
testX = du.featurewise_zero_center(testX, mean)
# Building Residual Network
net = zqtflearn.input_data(shape=[None, 28, 28, 1])
net = zqtflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
# Residual blocks
net = zqtflearn.residual_bottleneck(net, 3, 16, 64)
net = zqtflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = zqtflearn.residual_bottleneck(net, 2, 32, 128)
net = zqtflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)
net = zqtflearn.residual_bottleneck(net, 2, 64, 256)
net = zqtflearn.batch_normalization(net)
net = zqtflearn.activation(net, 'relu')
net = zqtflearn.global_avg_pool(net)
# Regression
net = zqtflearn.fully_connected(net, 10, activation='softmax')
net = zqtflearn.regression(net,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.1)
testY = zqtflearn.data_utils.to_categorical(testY)
# Real-time data preprocessing
img_prep = zqtflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
# Real-time data augmentation
img_aug = zqtflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
# Building Residual Network
net = zqtflearn.input_data(shape=[None, 32, 32, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
net = zqtflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = zqtflearn.residual_block(net, n, 16)
net = zqtflearn.residual_block(net, 1, 32, downsample=True)
net = zqtflearn.residual_block(net, n - 1, 32)
net = zqtflearn.residual_block(net, 1, 64, downsample=True)
net = zqtflearn.residual_block(net, n - 1, 64)
net = zqtflearn.batch_normalization(net)
net = zqtflearn.activation(net, 'relu')
net = zqtflearn.global_avg_pool(net)
# Regression
net = zqtflearn.fully_connected(net, 10, activation='softmax')
mom = zqtflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = zqtflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
# Training
model = zqtflearn.DNN(net, checkpoint_path='model_resnet_cifar10',