def test_dnn(self):
with tf.Graph().as_default():
X = [3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]
Y = [1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]
input = zqtflearn.input_data(shape=[None])
linear = zqtflearn.single_unit(input)
regression = zqtflearn.regression(linear, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
m = zqtflearn.DNN(regression)
# Testing fit and predict
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
res = m.predict([3.2])[0]
self.assertGreater(res, 1.3, "DNN test (linear regression) failed! with score: " + str(res) + " expected > 1.3")
self.assertLess(res, 1.8, "DNN test (linear regression) failed! with score: " + str(res) + " expected < 1.8")
# Testing save method
m.save("test_dnn.zqtflearn")
self.assertTrue(os.path.exists("test_dnn.zqtflearn.index"))
with tf.Graph().as_default():
input = zqtflearn.input_data(shape=[None])
linear = zqtflearn.single_unit(input)
regression = zqtflearn.regression(linear, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
m = zqtflearn.DNN(regression)
# Testing load method
m.load("test_dnn.zqtflearn")
res = m.predict([3.2])[0]
self.assertGreater(res, 1.3, "DNN test (linear regression) failed after loading model! score: " + str(res) + " expected > 1.3")
self.assertLess(res, 1.8, "DNN test (linear regression) failed after loading model! score: " + str(res) + " expected < 1.8")
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 __init__(self):
network = zqtflearn.input_data(shape=[None, 784], name="input")
network = self.make_core_network(network)
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
model = zqtflearn.DNN(network, tensorboard_verbose=0)
self.model = model
def test_vbs1(self):
with tf.Graph().as_default():
# 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 = X[:20, :, :, :]
Y = Y[:20, :]
testX = testX[:10, :, :, :]
testY = testY[:10, :]
# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
network = conv_2d(network,
32,
3,
activation='relu',
regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network,
64,
3,
activation='relu',
regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 10, activation='softmax')
network = regression(network,
optimizer='adam',
learning_rate=0.01,
loss='categorical_crossentropy',
name='target')
# Training
model = zqtflearn.DNN(network, tensorboard_verbose=3)
model.fit({'input': X}, {'target': Y},
n_epoch=1,
batch_size=10,
validation_set=({
'input': testX
}, {
'target': testY
}),
validation_batch_size=5,
snapshot_step=10,
show_metric=True,
run_id='convnet_mnist_vbs')
self.assertEqual(model.train_ops[0].validation_batch_size, 5)
self.assertEqual(model.train_ops[0].batch_size, 10)
def __init__(self):
# Building deep neural network
network = zqtflearn.input_data(shape=[None, 784], name="input")
network = self.make_core_network(network)
# Regression using SGD with learning rate decay and Top-3 accuracy
sgd = zqtflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)
top_k = zqtflearn.metrics.Top_k(3)
network = zqtflearn.regression(network, optimizer=sgd, metric=top_k,
loss='categorical_crossentropy', name="target")
model = zqtflearn.DNN(network, tensorboard_verbose=0)
self.model = model
def __init__(self):
inputs = zqtflearn.input_data(shape=[None, 784], name="input")
with tf.variable_scope("scope1") as scope:
net_conv = Model1.make_core_network(inputs) # shape (?, 10)
with tf.variable_scope("scope2") as scope:
net_dnn = Model2.make_core_network(inputs) # shape (?, 10)
network = tf.concat([net_conv, net_dnn], 1, name="concat") # shape (?, 20)
network = zqtflearn.fully_connected(network, 10, activation="softmax")
network = regression(network, optimizer='adam', learning_rate=0.01,
loss='categorical_crossentropy', name='target')
self.model = zqtflearn.DNN(network, tensorboard_verbose=0)
def test_recurrent_layers(self):
X = [[1, 3, 5, 7], [2, 4, 8, 10], [1, 5, 9, 11], [2, 6, 8, 0]]
Y = [[0., 1.], [1., 0.], [0., 1.], [1., 0.]]
with tf.Graph().as_default():
g = zqtflearn.input_data(shape=[None, 4])
g = zqtflearn.embedding(g, input_dim=12, output_dim=4)
g = zqtflearn.lstm(g, 6)
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=300, snapshot_epoch=False)
self.assertGreater(m.predict([[5, 9, 11, 1]])[0][1], 0.9)
def test_core_layers(self):
X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
Y_nand = [[1.], [1.], [1.], [0.]]
Y_or = [[0.], [1.], [1.], [1.]]
# Graph definition
with tf.Graph().as_default():
# Building a network with 2 optimizers
g = zqtflearn.input_data(shape=[None, 2])
# Nand operator definition
g_nand = zqtflearn.fully_connected(g, 32, activation='linear')
g_nand = zqtflearn.fully_connected(g_nand, 32, activation='linear')
g_nand = zqtflearn.fully_connected(g_nand, 1, activation='sigmoid')
g_nand = zqtflearn.regression(g_nand, optimizer='sgd',
learning_rate=2.,
loss='binary_crossentropy')
# Or operator definition
g_or = zqtflearn.fully_connected(g, 32, activation='linear')
g_or = zqtflearn.fully_connected(g_or, 32, activation='linear')
g_or = zqtflearn.fully_connected(g_or, 1, activation='sigmoid')
g_or = zqtflearn.regression(g_or, optimizer='sgd',
learning_rate=2.,
loss='binary_crossentropy')
# XOR merging Nand and Or operators
g_xor = zqtflearn.merge([g_nand, g_or], mode='elemwise_mul')
# Training
m = zqtflearn.DNN(g_xor)
m.fit(X, [Y_nand, Y_or], n_epoch=400, snapshot_epoch=False)
# Testing
self.assertLess(m.predict([[0., 0.]])[0][0], 0.01)
self.assertGreater(m.predict([[0., 1.]])[0][0], 0.9)
self.assertGreater(m.predict([[1., 0.]])[0][0], 0.9)
self.assertLess(m.predict([[1., 1.]])[0][0], 0.01)
# Bulk Tests
with tf.Graph().as_default():
net = zqtflearn.input_data(shape=[None, 2])
net = zqtflearn.flatten(net)
net = zqtflearn.reshape(net, new_shape=[-1])
net = zqtflearn.activation(net, 'relu')
net = zqtflearn.dropout(net, 0.5)
net = zqtflearn.single_unit(net)
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)
def build_simple_model(self):
"""Build a simple model for test
Returns:
DNN, [ (input layer name, input placeholder, input data) ], Target data
"""
inputPlaceholder1, inputPlaceholder2 = \
tf.placeholder(tf.float32, (1, 1), name = "input1"), tf.placeholder(tf.float32, (1, 1), name = "input2")
input1 = zqtflearn.input_data(placeholder=inputPlaceholder1)
input2 = zqtflearn.input_data(placeholder=inputPlaceholder2)
network = zqtflearn.merge([input1, input2], "sum")
network = zqtflearn.reshape(network, (1, 1))
network = zqtflearn.fully_connected(network, 1)
network = zqtflearn.regression(network)
return (
zqtflearn.DNN(network),
[("input1:0", inputPlaceholder1, self.INPUT_DATA_1),
("input2:0", inputPlaceholder2, self.INPUT_DATA_2)],
self.TARGET,
)
trainable_vars=disc_vars,
batch_size=64,
name='target_disc',
op_name='DISC')
gen_vars = zqtflearn.get_layer_variables_by_scope('Generator')
gan_model = zqtflearn.regression(stacked_gan_net,
optimizer='adam',
loss='categorical_crossentropy',
trainable_vars=gen_vars,
batch_size=64,
name='target_gen',
op_name='GEN')
# Define GAN model, that output the generated images.
gan = zqtflearn.DNN(gan_model)
# Training
# Prepare input data to feed to the discriminator
disc_noise = np.random.uniform(-1., 1., size=[total_samples, z_dim])
# Prepare target data to feed to the discriminator (0: fake image, 1: real image)
y_disc_fake = np.zeros(shape=[total_samples])
y_disc_real = np.ones(shape=[total_samples])
y_disc_fake = zqtflearn.data_utils.to_categorical(y_disc_fake, 2)
y_disc_real = zqtflearn.data_utils.to_categorical(y_disc_real, 2)
# Prepare input data to feed to the stacked generator/discriminator
gen_noise = np.random.uniform(-1., 1., size=[total_samples, z_dim])
# Prepare target data to feed to the discriminator
# Generator tries to fool the discriminator, thus target is 1 (e.g. real images)
y_gen = np.ones(shape=[total_samples])
X_test = h5f['cifar10_X_test']
Y_test = h5f['cifar10_Y_test']
# Build network
network = input_data(shape=[None, 32, 32, 3], dtype=tf.float32)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 10, activation='softmax')
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
# Training
model = zqtflearn.DNN(network, tensorboard_verbose=0)
model.fit(X,
Y,
n_epoch=50,
shuffle=True,
validation_set=(X_test, Y_test),
show_metric=True,
batch_size=96,
run_id='cifar10_cnn')
h5f.close()
def build_model(self, learning_rate=[0.001, 0.01]):
'''
Model - wide and deep - built using zqtflearn
'''
n_cc = len(self.continuous_columns)
n_cc = 108
input_shape = [None, n_cc]
if self.verbose:
print("=" * 77 + " Model %s (type=%s)" %
(self.name, self.model_type))
print(" Input placeholder shape=%s" % str(input_shape))
wide_inputs = zqtflearn.input_data(shape=input_shape, name="wide_X")
deep_inputs = zqtflearn.input_data(shape=[None, 1], name="deep_X")
if not isinstance(learning_rate, list):
learning_rate = [learning_rate, learning_rate] # wide, deep
if self.verbose:
print(" Learning rates (wide, deep)=%s" % learning_rate)
with tf.name_scope(
"Y"): # placeholder for target variable (i.e. trainY input)
Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y")
with tf.variable_op_scope([wide_inputs], None, "cb_unit",
reuse=False) as scope:
central_bias = zqtflearn.variables.variable(
'central_bias',
shape=[1],
initializer=tf.constant_initializer(np.random.randn()),
trainable=True,
restore=True)
tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/cb_unit',
central_bias)
if 'wide' in self.model_type:
wide_network = self.wide_model(wide_inputs, n_cc)
network = wide_network
wide_network_with_bias = tf.add(wide_network,
central_bias,
name="wide_with_bias")
if 'deep' in self.model_type:
deep_network = self.deep_model(
wide_inputs, deep_inputs, n_cc
) # 这里面是wide inputs,在这个函数内部wide_inputs,会和deep_model制造的输入相合并。
deep_network_with_bias = tf.add(deep_network,
central_bias,
name="deep_with_bias")
if 'wide' in self.model_type:
network = tf.add(wide_network, deep_network)
if self.verbose:
print("Wide + deep model network %s" % network)
else:
network = deep_network
network = tf.add(network, central_bias, name="add_central_bias")
# add validation monitor summaries giving confusion matrix entries
with tf.name_scope('Monitors'):
predictions = tf.cast(tf.greater(network, 0), tf.int64)
print("predictions=%s" % predictions)
Ybool = tf.cast(Y_in, tf.bool)
print("Ybool=%s" % Ybool)
pos = tf.boolean_mask(predictions, Ybool)
neg = tf.boolean_mask(predictions, ~Ybool)
psize = tf.cast(tf.shape(pos)[0], tf.int64)
nsize = tf.cast(tf.shape(neg)[0], tf.int64)
true_positive = tf.reduce_sum(pos, name="true_positive")
false_negative = tf.subtract(psize,
true_positive,
name="false_negative")
false_positive = tf.reduce_sum(neg, name="false_positive")
true_negative = tf.subtract(nsize,
false_positive,
name="true_negative")
overall_accuracy = tf.truediv(tf.add(true_positive, true_negative),
tf.add(nsize, psize),
name="overall_accuracy")
vmset = [
true_positive, true_negative, false_positive, false_negative,
overall_accuracy
]
trainable_vars = tf.trainable_variables()
tv_deep = [v for v in trainable_vars if v.name.startswith('deep_')]
tv_wide = [v for v in trainable_vars if v.name.startswith('wide_')]
if self.verbose:
print("DEEP trainable_vars")
for v in tv_deep:
print(" Variable %s: %s" % (v.name, v))
print("WIDE trainable_vars")
for v in tv_wide:
print(" Variable %s: %s" % (v.name, v))
# if 'wide' in self.model_type:
# if not 'deep' in self.model_type:
# tv_wide.append(central_bias)
# zqtflearn.regression(wide_network_with_bias,
# placeholder=Y_in,
# optimizer='sgd',
# loss='roc_auc_score',
# #loss='binary_crossentropy',
# metric="accuracy",
# learning_rate=learning_rate[0],
# validation_monitors=vmset,
# trainable_vars=tv_wide,
# op_name="wide_regression",
# name="Y")
#
# if 'deep' in self.model_type:
# if not 'wide' in self.model_type:
# tv_wide.append(central_bias)
# zqtflearn.regression(deep_network_with_bias,
# placeholder=Y_in,
# optimizer='adam',
# loss='roc_auc_score',
# #loss='binary_crossentropy',
# metric="accuracy",
# learning_rate=learning_rate[1],
# validation_monitors=vmset if not 'wide' in self.model_type else None,
# trainable_vars=tv_deep,
# op_name="deep_regression",
# name="Y")
if self.model_type == 'wide+deep': # learn central bias separately for wide+deep
zqtflearn.regression(
network,
placeholder=Y_in,
optimizer='adam',
#loss="roc_auc_score",
loss='binary_crossentropy',
metric="accuracy",
validation_monitors=vmset,
learning_rate=learning_rate[0], # use wide learning rate
#trainable_vars=[central_bias], #[tv_deep,tv_wide,central_bias] # None
op_name="central_bias_regression",
name="Y")
self.model = zqtflearn.DNN(
network,
tensorboard_verbose=self.tensorboard_verbose,
max_checkpoints=self.max_checkpoints,
checkpoint_path="%s/%s.tfl" % (self.checkpoints_dir, self.name),
tensorboard_dir=self.tensorboard_dir)
# tensorboard_dir="/tmp/tflearn_logs/" zqtflearn.DNN 我把他改为当前目录下的了，这样也比较好规范
if 'deep' in self.model_type:
embeddingWeights = zqtflearn.get_layer_variables_by_name(
'deep_video_ids_embed')[0]
# CUSTOM_WEIGHT = pickle.load("Haven't deal")
# emb = np.array(CUSTOM_WEIGHT, dtype=np.float32)
# emb = self.embedding
new_emb_t = tf.convert_to_tensor(self.embedding)
self.model.set_weights(embeddingWeights, new_emb_t)
if self.verbose:
print("Target variables:")
for v in tf.get_collection(tf.GraphKeys.TARGETS):
print(" variable %s: %s" % (v.name, v))
print("=" * 77)
print("model build finish")
def build_model(self, learning_rate=[0.001, 0.01]):
'''
Model - wide and deep - built using zqtflearn
'''
n_cc = len(self.continuous_columns)
n_categories = 1 # two categories: is_idv and is_not_idv
input_shape = [None, n_cc]
if self.verbose:
print ("="*77 + " Model %s (type=%s)" % (self.name, self.model_type))
print (" Input placeholder shape=%s" % str(input_shape))
wide_inputs = zqtflearn.input_data(shape=input_shape, name="wide_X")
if not isinstance(learning_rate, list):
learning_rate = [learning_rate, learning_rate] # wide, deep
if self.verbose:
print (" Learning rates (wide, deep)=%s" % learning_rate)
with tf.name_scope("Y"): # placeholder for target variable (i.e. trainY input)
Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y")
with tf.variable_scope(None, "cb_unit", [wide_inputs]) as scope:
central_bias = zqtflearn.variables.variable('central_bias', shape=[1],
initializer=tf.constant_initializer(np.random.randn()),
trainable=True, restore=True)
tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/cb_unit', central_bias)
if 'wide' in self.model_type:
wide_network = self.wide_model(wide_inputs, n_cc)
network = wide_network
wide_network_with_bias = tf.add(wide_network, central_bias, name="wide_with_bias")
if 'deep' in self.model_type:
deep_network = self.deep_model(wide_inputs, n_cc)
deep_network_with_bias = tf.add(deep_network, central_bias, name="deep_with_bias")
if 'wide' in self.model_type:
network = tf.add(wide_network, deep_network)
if self.verbose:
print ("Wide + deep model network %s" % network)
else:
network = deep_network
network = tf.add(network, central_bias, name="add_central_bias")
# add validation monitor summaries giving confusion matrix entries
with tf.name_scope('Monitors'):
predictions = tf.cast(tf.greater(network, 0), tf.int64)
print ("predictions=%s" % predictions)
Ybool = tf.cast(Y_in, tf.bool)
print ("Ybool=%s" % Ybool)
pos = tf.boolean_mask(predictions, Ybool)
neg = tf.boolean_mask(predictions, ~Ybool)
psize = tf.cast(tf.shape(pos)[0], tf.int64)
nsize = tf.cast(tf.shape(neg)[0], tf.int64)
true_positive = tf.reduce_sum(pos, name="true_positive")
false_negative = tf.subtract(psize, true_positive, name="false_negative")
false_positive = tf.reduce_sum(neg, name="false_positive")
true_negative = tf.subtract(nsize, false_positive, name="true_negative")
overall_accuracy = tf.truediv(tf.add(true_positive, true_negative), tf.add(nsize, psize), name="overall_accuracy")
vmset = [true_positive, true_negative, false_positive, false_negative, overall_accuracy]
trainable_vars = tf.trainable_variables()
tv_deep = [v for v in trainable_vars if v.name.startswith('deep_')]
tv_wide = [v for v in trainable_vars if v.name.startswith('wide_')]
if self.verbose:
print ("DEEP trainable_vars")
for v in tv_deep:
print (" Variable %s: %s" % (v.name, v))
print ("WIDE trainable_vars")
for v in tv_wide:
print (" Variable %s: %s" % (v.name, v))
if 'wide' in self.model_type:
if not 'deep' in self.model_type:
tv_wide.append(central_bias)
zqtflearn.regression(wide_network_with_bias,
placeholder=Y_in,
optimizer='sgd',
#loss='roc_auc_score',
loss='binary_crossentropy',
metric="accuracy",
learning_rate=learning_rate[0],
validation_monitors=vmset,
trainable_vars=tv_wide,
op_name="wide_regression",
name="Y")
if 'deep' in self.model_type:
if not 'wide' in self.model_type:
tv_wide.append(central_bias)
zqtflearn.regression(deep_network_with_bias,
placeholder=Y_in,
optimizer='adam',
#loss='roc_auc_score',
loss='binary_crossentropy',
metric="accuracy",
learning_rate=learning_rate[1],
validation_monitors=vmset if not 'wide' in self.model_type else None,
trainable_vars=tv_deep,
op_name="deep_regression",
name="Y")
if self.model_type=='wide+deep': # learn central bias separately for wide+deep
zqtflearn.regression(network,
placeholder=Y_in,
optimizer='adam',
loss='binary_crossentropy',
metric="accuracy",
learning_rate=learning_rate[0], # use wide learning rate
trainable_vars=[central_bias],
op_name="central_bias_regression",
name="Y")
self.model = zqtflearn.DNN(network,
tensorboard_verbose=self.tensorboard_verbose,
max_checkpoints=5,
checkpoint_path="%s/%s.tfl" % (self.checkpoints_dir, self.name),
)
if self.verbose:
print ("Target variables:")
for v in tf.get_collection(tf.GraphKeys.TARGETS):
print (" variable %s: %s" % (v.name, v))
print ("="*77)
+ (1 - x_true) * tf.log(1e-10 + 1 - x_reconstructed)
encode_decode_loss = -tf.reduce_sum(encode_decode_loss, 1)
# KL Divergence loss
kl_div_loss = 1 + z_std - tf.square(z_mean) - tf.exp(z_std)
kl_div_loss = -0.5 * tf.reduce_sum(kl_div_loss, 1)
return tf.reduce_mean(encode_decode_loss + kl_div_loss)
net = zqtflearn.regression(decoder, optimizer='rmsprop', learning_rate=0.001,
loss=vae_loss, metric=None, name='target_images')
# We will need 2 models, one for training that will learn the latent
# representation, and one that can take random normal noise as input and
# use the decoder part of the network to generate an image
# Train the VAE
training_model = zqtflearn.DNN(net, tensorboard_verbose=0)
training_model.fit({'input_images': X}, {'target_images': X}, n_epoch=100,
validation_set=(testX, testX), batch_size=256, run_id="vae")
# Build an image generator (re-using the decoding layers)
# Input data is a normal (gaussian) random distribution (with dim = latent_dim)
input_noise = zqtflearn.input_data(shape=[None, latent_dim], name='input_noise')
decoder = zqtflearn.fully_connected(input_noise, hidden_dim, activation='relu',
scope='decoder_h', reuse=True)
decoder = zqtflearn.fully_connected(decoder, original_dim, activation='sigmoid',
scope='decoder_out', reuse=True)
generator_model = zqtflearn.DNN(decoder, session=training_model.session)
# Building a manifold of generated digits
n = 25 # Figure row size
figure = np.zeros((28 * n, 28 * n))
activation='relu',
name='block5_conv2')
block5_conv3 = conv_2d(block5_conv2,
512,
3,
activation='relu',
name='block5_conv3')
block5_conv4 = conv_2d(block5_conv3,
512,
3,
activation='relu',
name='block5_conv4')
block4_pool = max_pool_2d(block5_conv4, 2, strides=2, name='block4_pool')
flatten_layer = zqtflearn.layers.core.flatten(block4_pool, name='Flatten')
fc1 = fully_connected(flatten_layer, 4096, activation='relu')
dp1 = dropout(fc1, 0.5)
fc2 = fully_connected(dp1, 4096, activation='relu')
dp2 = dropout(fc2, 0.5)
network = fully_connected(dp2, 1000, activation='rmsprop')
regression = zqtflearn.regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
model = zqtflearn.DNN(regression,
checkpoint_path='vgg19',
tensorboard_dir="./logs")
def test_dnn_loading_scope(self):
with tf.Graph().as_default():
X = [
3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1
]
Y = [
1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,
2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3
]
input = zqtflearn.input_data(shape=[None])
linear = zqtflearn.single_unit(input)
regression = zqtflearn.regression(linear,
optimizer='sgd',
loss='mean_square',
metric='R2',
learning_rate=0.01)
m = zqtflearn.DNN(regression)
# Testing fit and predict
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
res = m.predict([3.2])[0]
self.assertGreater(
res, 1.3, "DNN test (linear regression) failed! with score: " +
str(res) + " expected > 1.3")
self.assertLess(
res, 1.8, "DNN test (linear regression) failed! with score: " +
str(res) + " expected < 1.8")
# Testing save method
m.save("test_dnn.zqtflearn")
self.assertTrue(os.path.exists("test_dnn.zqtflearn.index"))
# Testing loading, with change of variable scope (saved with no scope, now loading into scopeA)
with tf.Graph().as_default(): # start with clear graph
with tf.variable_scope("scopeA") as scope:
input = zqtflearn.input_data(shape=[None])
linear = zqtflearn.single_unit(input)
regression = zqtflearn.regression(linear,
optimizer='sgd',
loss='mean_square',
metric='R2',
learning_rate=0.01)
m = zqtflearn.DNN(regression)
def try_load():
m.load("test_dnn.zqtflearn")
self.assertRaises(
tf.errors.NotFoundError,
try_load) # fails, since names in file don't have "scopeA"
m.load("test_dnn.zqtflearn", variable_name_map=(
"scopeA/",
"")) # succeeds, because variable names are rewritten
res = m.predict([3.2])[0]
self.assertGreater(
res, 1.3,
"DNN test (linear regression) failed after loading model! score: "
+ str(res) + " expected > 1.3")
self.assertLess(
res, 1.8,
"DNN test (linear regression) failed after loading model! score: "
+ str(res) + " expected < 1.8")
# 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)
# Training
model = zqtflearn.DNN(net,
checkpoint_path='model_resnet_mnist',
max_checkpoints=10,
tensorboard_verbose=0)
model.fit(X,
Y,
n_epoch=100,
validation_set=(testX, testY),
show_metric=True,
batch_size=256,
run_id='resnet_mnist')
# categorical_labels=True, normalize=True,
# files_extension=['.jpg', '.png'], filter_channel=True)
num_classes = 10 # num of your dataset
# VGG preprocessing
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center(mean=[123.68, 116.779, 103.939],
per_channel=True)
# VGG Network
x = zqtflearn.input_data(shape=[None, 224, 224, 3], name='input',
data_preprocessing=img_prep)
softmax = vgg16(x, num_classes)
regression = zqtflearn.regression(softmax, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001, restore=False)
model = zqtflearn.DNN(regression, checkpoint_path='vgg-finetuning',
max_checkpoints=3, tensorboard_verbose=2,
tensorboard_dir="./logs")
model_file = os.path.join(model_path, "vgg16.zqtflearn")
model.load(model_file, weights_only=True)
# Start finetuning
model.fit(X, Y, n_epoch=10, validation_set=0.1, shuffle=True,
show_metric=True, batch_size=64, snapshot_epoch=False,
snapshot_step=200, run_id='vgg-finetuning')
model.save('your-task-model-retrained-by-vgg')
encoder = zqtflearn.fully_connected(encoder, 256)
encoder = zqtflearn.fully_connected(encoder, 64)
# Building the decoder
decoder = zqtflearn.fully_connected(encoder, 256)
decoder = zqtflearn.fully_connected(decoder, 784, activation='sigmoid')
# Regression, with mean square error
net = zqtflearn.regression(decoder,
optimizer='adam',
learning_rate=0.001,
loss='mean_square',
metric=None)
# Training the auto encoder
model = zqtflearn.DNN(net, tensorboard_verbose=0)
model.fit(X,
X,
n_epoch=20,
validation_set=(testX, testX),
run_id="auto_encoder",
batch_size=256)
# Encoding X[0] for test
print("\nTest encoding of X[0]:")
# New model, re-using the same session, for weights sharing
encoding_model = zqtflearn.DNN(encoder, session=model.session)
print(encoding_model.predict([X[0]]))
# Testing the image reconstruction on new data (test set)
print("\nVisualizing results after being encoded and decoded:")
activation=None,
name='Conv2d_7b_1x1')))
net = avg_pool_2d(net,
net.get_shape().as_list()[1:3],
strides=2,
padding='VALID',
name='AvgPool_1a_8x8')
net = flatten(net)
net = dropout(net, dropout_keep_prob)
loss = fully_connected(net, num_classes, activation='softmax')
network = zqtflearn.regression(loss,
optimizer='RMSprop',
loss='categorical_crossentropy',
learning_rate=0.0001)
model = zqtflearn.DNN(network,
checkpoint_path='inception_resnet_v2',
max_checkpoints=1,
tensorboard_verbose=2,
tensorboard_dir="./tflearn_logs/")
model.fit(X,
Y,
n_epoch=1000,
validation_set=0.1,
shuffle=True,
show_metric=True,
batch_size=32,
snapshot_step=2000,
snapshot_epoch=False,
run_id='inception_resnet_v2_17flowers')
network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = conv_2d(network, 512, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = fully_connected(network, 4096, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 17, activation='softmax')
network = regression(network,
optimizer='rmsprop',
loss='categorical_crossentropy',
learning_rate=0.0001)
# Training
model = zqtflearn.DNN(network,
checkpoint_path='model_vgg',
max_checkpoints=1,
tensorboard_verbose=0)
model.fit(X,
Y,
n_epoch=500,
shuffle=True,
show_metric=True,
batch_size=32,
snapshot_step=500,
snapshot_epoch=False,
run_id='vgg_oxflowers17')
# MNIST Data
X, Y, testX, testY = mnist.load_data(one_hot=True)
# Model
input_layer = zqtflearn.input_data(shape=[None, 784], name='input')
dense1 = zqtflearn.fully_connected(input_layer, 128, name='dense1')
dense2 = zqtflearn.fully_connected(dense1, 256, name='dense2')
softmax = zqtflearn.fully_connected(dense2, 10, activation='softmax')
regression = zqtflearn.regression(softmax,
optimizer='adam',
learning_rate=0.001,
loss='categorical_crossentropy')
# Define classifier, with model checkpoint (autosave)
model = zqtflearn.DNN(regression, checkpoint_path='model.tfl.ckpt')
# Train model, with model checkpoint every epoch and every 200 training steps.
model.fit(
X,
Y,
n_epoch=1,
validation_set=(testX, testY),
show_metric=True,
snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch.
snapshot_step=500, # Snapshot (save & evalaute) model every 500 steps.
run_id='model_and_weights')
# ---------------------
# Save and load a model
# ---------------------
passengers[i][1] = 1. if passengers[i][1] == 'female' else 0.
return np.array(passengers, dtype=np.float32)
# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore=[1, 6]
# Preprocess data
data = preprocess(data, to_ignore)
# Build neural network
net = zqtflearn.input_data(shape=[None, 6])
net = zqtflearn.fully_connected(net, 32)
net = zqtflearn.fully_connected(net, 32)
net = zqtflearn.fully_connected(net, 2, activation='softmax')
net = zqtflearn.regression(net)
# Define model
model = zqtflearn.DNN(net)
# Start training (apply gradient descent algorithm)
model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
# Let's create some data for DiCaprio and Winslet
dicaprio = [3, 'Jack Dawson', 'male', 19, 0, 0, 'N/A', 5.0000]
winslet = [1, 'Rose DeWitt Bukater', 'female', 17, 1, 2, 'N/A', 100.0000]
# Preprocess data
dicaprio, winslet = preprocess([dicaprio, winslet], to_ignore)
# Predict surviving chances (class 1 results)
pred = model.predict([dicaprio, winslet])
print("DiCaprio Surviving Rate:", pred[0][1])
print("Winslet Surviving Rate:", pred[1][1])
def build_model(self, learning_rate=[0.001, 0.01]):
'''
Model - wide and deep - built using tflearn
'''
n_cc = len(self.continuous_columns)
n_cc = 108
input_shape = [None, n_cc]
if self.verbose:
print("=" * 77 + " Model %s (type=%s)" %
(self.name, self.model_type))
print(" Input placeholder shape=%s" % str(input_shape))
wide_inputs = zqtflearn.input_data(shape=input_shape, name="wide_X")
if not isinstance(learning_rate, list):
learning_rate = [learning_rate, learning_rate] # wide, deep
if self.verbose:
print(" Learning rates (wide, deep)=%s" % learning_rate)
with tf.name_scope(
"Y"): # placeholder for target variable (i.e. trainY input)
Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y")
with tf.variable_op_scope([wide_inputs], None, "cb_unit",
reuse=False) as scope:
central_bias = zqtflearn.variables.variable(
'central_bias',
shape=[1],
initializer=tf.constant_initializer(np.random.randn()),
trainable=True,
restore=True)
tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/cb_unit',
central_bias)
wide_network = self.wide_model(wide_inputs, n_cc)
network = wide_network
network = tf.add(network, central_bias, name="add_central_bias")
# add validation monitor summaries giving confusion matrix entries
with tf.name_scope('Monitors'):
predictions = tf.cast(tf.greater(network, 0), tf.int64)
print("predictions=%s" % predictions)
Ybool = tf.cast(Y_in, tf.bool)
print("Ybool=%s" % Ybool)
pos = tf.boolean_mask(predictions, Ybool)
neg = tf.boolean_mask(predictions, ~Ybool)
psize = tf.cast(tf.shape(pos)[0], tf.int64)
nsize = tf.cast(tf.shape(neg)[0], tf.int64)
true_positive = tf.reduce_sum(pos, name="true_positive")
false_negative = tf.subtract(psize,
true_positive,
name="false_negative")
false_positive = tf.reduce_sum(neg, name="false_positive")
true_negative = tf.subtract(nsize,
false_positive,
name="true_negative")
overall_accuracy = tf.truediv(tf.add(true_positive, true_negative),
tf.add(nsize, psize),
name="overall_accuracy")
vmset = [
true_positive, true_negative, false_positive, false_negative,
overall_accuracy
]
zqtflearn.regression(
network,
placeholder=Y_in,
optimizer='adam',
#loss="roc_auc_score",
loss='binary_crossentropy',
metric="accuracy",
learning_rate=learning_rate[0], # use wide learning rate
# trainable_vars=[central_bias],
validation_monitors=vmset,
op_name="central_bias_regression",
name="Y")
self.model = zqtflearn.DNN(
network,
tensorboard_verbose=self.tensorboard_verbose,
max_checkpoints=self.max_checkpoints,
checkpoint_path="%s/%s.tfl" % (self.checkpoints_dir, self.name),
tensorboard_dir=self.tensorboard_dir)
if self.verbose:
print("Target variables:")
for v in tf.get_collection(tf.GraphKeys.TARGETS):
print(" variable %s: %s" % (v.name, v))
print("=" * 77)
import zqtflearn
# Regression data
X = [
3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042, 10.791,
5.313, 7.997, 5.654, 9.27, 3.1
]
Y = [
1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827,
3.465, 1.65, 2.904, 2.42, 2.94, 1.3
]
# Linear Regression graph
input_ = zqtflearn.input_data(shape=[None])
linear = zqtflearn.single_unit(input_)
regression = zqtflearn.regression(linear,
optimizer='sgd',
loss='mean_square',
metric='R2',
learning_rate=0.01)
m = zqtflearn.DNN(regression)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
print("\nRegression result:")
print("Y = " + str(m.get_weights(linear.W)) + "*X + " +
str(m.get_weights(linear.b)))
print("\nTest prediction for x = 3.2, 3.3, 3.4:")
print(m.predict([3.2, 3.3, 3.4]))
# should output (close, not exact) y = [1.5315033197402954, 1.5585315227508545, 1.5855598449707031]
"""
from __future__ import absolute_import, division, print_function
import zqtflearn
import numpy as np
# Regression data- 10 training instances
#10 input features per instance.
X=np.random.rand(10,10).tolist()
#2 output features per instance
Y=np.random.rand(10,2).tolist()
# Multiple Regression graph, 10-d input layer
input_ = zqtflearn.input_data(shape=[None, 10])
#10-d fully connected layer
r1 = zqtflearn.fully_connected(input_, 10)
#2-d fully connected layer for output
r1 = zqtflearn.fully_connected(r1, 2)
r1 = zqtflearn.regression(r1, optimizer='sgd', loss='mean_square',
metric='R2', learning_rate=0.01)
m = zqtflearn.DNN(r1)
m.fit(X,Y, n_epoch=100, show_metric=True, snapshot_epoch=False)
#Predict for 1 instance
testinstance=np.random.rand(1,10).tolist()
print("\nInput features: ",testinstance)
print("\n Predicted output: ")
print(m.predict(testinstance))
X = [[0.], [1.]]
Y = [[1.], [0.]]
# Graph definition
with tf.Graph().as_default():
g = zqtflearn.input_data(shape=[None, 1])
g = zqtflearn.fully_connected(g, 128, activation='linear')
g = zqtflearn.fully_connected(g, 128, activation='linear')
g = zqtflearn.fully_connected(g, 1, activation='sigmoid')
g = zqtflearn.regression(g,
optimizer='sgd',
learning_rate=2.,
loss='mean_square')
# Model training
m = zqtflearn.DNN(g)
m.fit(X, Y, n_epoch=100, snapshot_epoch=False)
# Test model
print("Testing NOT operator")
print("NOT 0:", m.predict([[0.]]))
print("NOT 1:", m.predict([[1.]]))
# Logical OR operator
X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
Y = [[0.], [1.], [1.], [1.]]
# Graph definition
with tf.Graph().as_default():
g = zqtflearn.input_data(shape=[None, 2])
g = zqtflearn.fully_connected(g, 128, activation='linear')
def test_vm1(self):
with tf.Graph().as_default():
# 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 = X[:10, :, :, :]
Y = Y[:10, :]
# Building convolutional network
network = input_data(shape=[None, 28, 28, 1], name='input')
network = conv_2d(network,
32,
3,
activation='relu',
regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = conv_2d(network,
64,
3,
activation='relu',
regularizer="L2")
network = max_pool_2d(network, 2)
network = local_response_normalization(network)
network = fully_connected(network, 128, activation='tanh')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='tanh')
network = dropout(network, 0.8)
# construct two varaibles to add as additional "valiation monitors"
# these varaibles are evaluated each time validation happens (eg at a snapshot)
# and the results are summarized and output to the tensorboard events file,
# together with the accuracy and loss plots.
#
# Here, we generate a dummy variable given by the sum over the current
# network tensor, and a constant variable. In practice, the validation
# monitor may present useful information, like confusion matrix
# entries, or an AUC metric.
with tf.name_scope('CustomMonitor'):
test_var = tf.reduce_sum(tf.cast(network, tf.float32),
name="test_var")
test_const = tf.constant(32.0, name="custom_constant")
print("network=%s, test_var=%s" % (network, test_var))
network = fully_connected(network, 10, activation='softmax')
network = regression(network,
optimizer='adam',
learning_rate=0.01,
loss='categorical_crossentropy',
name='target',
validation_monitors=[test_var, test_const])
# Training
model = zqtflearn.DNN(network, tensorboard_verbose=3)
model.fit({'input': X}, {'target': Y},
n_epoch=1,
validation_set=({
'input': testX
}, {
'target': testY
}),
snapshot_step=10,
show_metric=True,
run_id='convnet_mnist')
# check for validation monitor variables
ats = tf.get_collection("Adam_testing_summaries")
print("ats=%s" % ats)
self.assertTrue(
len(ats) == 4
) # should be four variables being summarized: [loss, test_var, test_const, accuracy]
session = model.session
print("session=%s" % session)
trainer = model.trainer
print("train_ops = %s" % trainer.train_ops)
top = trainer.train_ops[0]
vmtset = top.validation_monitors_T
print("validation_monitors_T = %s" % vmtset)
with model.session.as_default():
ats_var_val = zqtflearn.variables.get_value(vmtset[0])
ats_const_val = zqtflearn.variables.get_value(vmtset[1])
print("summary values: var=%s, const=%s" %
(ats_var_val, ats_const_val))
self.assertTrue(
ats_const_val ==
32) # test to make sure the constant made it through
from zqtflearn.layers.embedding_ops import embedding
from zqtflearn.layers.recurrent import bidirectional_rnn, BasicLSTMCell
from zqtflearn.layers.estimator import regression
# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl',
n_words=10000,
valid_portion=0.1)
trainX, trainY = train
testX, testY = test
# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=200, value=0.)
testX = pad_sequences(testX, maxlen=200, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY)
testY = to_categorical(testY)
# Network building
net = input_data(shape=[None, 200])
net = embedding(net, input_dim=20000, output_dim=128)
net = bidirectional_rnn(net, BasicLSTMCell(128), BasicLSTMCell(128))
net = dropout(net, 0.5)
net = fully_connected(net, 2, activation='softmax')
net = regression(net, optimizer='adam', loss='categorical_crossentropy')
# Training
model = zqtflearn.DNN(net, clip_gradients=0., tensorboard_verbose=2)
model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=64)
