def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF._get_session()
if self.histogram_freq and not self.merged:
mod_type = self.model.get_config()['name']
if mod_type == 'Sequential':
layers = {l.get_config()['name']: l for l in self.model.layers}
elif mod_type == 'Graph':
layers = self.model.nodes
else:
raise Exception('Unrecognized model:',
self.model.get_config()['name'])
for l in layers:
cur_layer = layers[l]
if hasattr(cur_layer, 'W'):
tf.histogram_summary('{}_W'.format(l), cur_layer.W)
if hasattr(cur_layer, 'b'):
tf.histogram_summary('{}_b'.format(l), cur_layer.b)
if hasattr(cur_layer, 'get_output'):
tf.histogram_summary('{}_out'.format(l),
cur_layer.get_output())
self.merged = tf.merge_all_summaries()
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF._get_session()
if self.histogram_freq:
mod_type = self.model.get_config()['name']
if mod_type == 'Sequential':
layers = {l.get_config()['name']: l for l in self.model.layers}
elif mod_type == 'Graph':
layers = self.model.nodes
else:
raise Exception('Unrecognized model:',
self.model.get_config()['name'])
for l in layers:
cur_layer = layers[l]
if hasattr(cur_layer, 'W'):
tf.histogram_summary('{}_W'.format(l), cur_layer.W)
if hasattr(cur_layer, 'b'):
tf.histogram_summary('{}_b'.format(l), cur_layer.b)
if hasattr(cur_layer, 'get_output'):
tf.histogram_summary('{}_out'.format(l),
cur_layer.get_output())
self.merged = tf.merge_all_summaries()
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
def generate(self, Z=None):
if Z is None:
Z = np.random.normal(0, 1, self.latent_shape)
session = tensorflow_backend._get_session()
return session.run([self.x_hat_mu, self.x_hat_log_sigma],
feed_dict={self.z: Z})
def fit(self, X, num_epochs=1):
session = tensorflow_backend._get_session()
writer_file = '/tmp/tmp0UWgeI' #mkdtemp()
print(writer_file)
writer = tf.train.SummaryWriter(writer_file, session.graph_def)
for batch_idx in range(num_epochs):
errors = []
for x in batch(X, self.batch_size):
errors.append(self.fit_batch(x, session))
print('({}) Epoch error: {}'.format(batch_idx, np.mean(errors)))
def fit(self, X, num_epochs=1):
session = tensorflow_backend._get_session()
writer_file = '/tmp/tmp0UWgeI'#mkdtemp()
print(writer_file)
writer = tf.train.SummaryWriter(writer_file, session.graph_def)
for batch_idx in range(num_epochs):
errors = []
for x in batch(X, self.batch_size):
errors.append(self.fit_batch(x, session))
print('({}) Epoch error: {}'.format(batch_idx, np.mean(errors)))
def test_TensorBoard():
import shutil
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
old_session = KTF._get_session()
filepath = './logs'
(X_train, y_train), (X_test,
y_test) = get_test_data(nb_train=train_samples,
nb_test=test_samples,
input_shape=(input_dim, ),
classification=True,
nb_class=nb_class)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
# case 1 Sequential wo accuracy
with tf.Graph().as_default():
session = tf.Session('')
KTF._set_session(session)
model = Sequential()
model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(nb_class, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd')
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
model.fit(X_train,
y_train,
batch_size=batch_size,
show_accuracy=True,
validation_data=(X_test, y_test),
callbacks=cbks,
nb_epoch=2)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
# case 2 Sequential w accuracy
with tf.Graph().as_default():
session = tf.Session('')
KTF._set_session(session)
model = Sequential()
model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(nb_class, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd')
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
model.fit(X_train,
y_train,
batch_size=batch_size,
show_accuracy=True,
validation_data=(X_test, y_test),
callbacks=cbks,
nb_epoch=2)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
# case 3 Graph
with tf.Graph().as_default():
session = tf.Session('')
KTF._set_session(session)
model = Graph()
model.add_input(name='X_vars', input_shape=(input_dim, ))
model.add_node(Dense(nb_hidden, activation="sigmoid"),
name='Dense1',
input='X_vars')
model.add_node(Dense(nb_class, activation="softmax"),
name='last_dense',
input='Dense1')
model.add_output(name='output', input='last_dense')
model.compile(optimizer='sgd', loss={'output': 'mse'})
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
model.fit({
'X_vars': X_train,
'output': y_train
},
batch_size=batch_size,
validation_data={
'X_vars': X_test,
'output': y_test
},
callbacks=cbks,
nb_epoch=2)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
KTF._set_session(old_session)
def test_TensorBoard():
import shutil
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
old_session = KTF._get_session()
filepath = './logs'
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples,
nb_test=test_samples,
input_shape=(input_dim,),
classification=True,
nb_class=nb_class)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
# case 1 Sequential wo accuracy
with tf.Graph().as_default():
session = tf.Session('')
KTF._set_session(session)
model = Sequential()
model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(nb_class, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd')
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
model.fit(X_train, y_train, batch_size=batch_size, show_accuracy=True,
validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
# case 2 Sequential w accuracy
with tf.Graph().as_default():
session = tf.Session('')
KTF._set_session(session)
model = Sequential()
model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(nb_class, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd')
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
model.fit(X_train, y_train, batch_size=batch_size, show_accuracy=True,
validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
# case 3 Graph
with tf.Graph().as_default():
session = tf.Session('')
KTF._set_session(session)
model = Graph()
model.add_input(name='X_vars', input_shape=(input_dim, ))
model.add_node(Dense(nb_hidden, activation="sigmoid"),
name='Dense1', input='X_vars')
model.add_node(Dense(nb_class, activation="softmax"),
name='last_dense',
input='Dense1')
model.add_output(name='output', input='last_dense')
model.compile(optimizer='sgd', loss={'output': 'mse'})
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
model.fit({'X_vars': X_train, 'output': y_train},
batch_size=batch_size,
validation_data={'X_vars': X_test, 'output': y_test},
callbacks=cbks, nb_epoch=2)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
KTF._set_session(old_session)
def encode(self, X):
session = tensorflow_backend._get_session()
return session.run([self.mu, self.log_sigma], feed_dict={self.x: X})
def reconstruct(self, X):
session = tensorflow_backend._get_session()
return session.run([self.x_hat_mu, self.x_hat_log_sigma],
feed_dict={self.x: X})
def generate(self, Z=None):
if Z is None:
Z = np.random.normal(0, 1, self.latent_shape)
session = tensorflow_backend._get_session()
return session.run([self.x_hat_mu, self.x_hat_log_sigma], feed_dict={self.z: Z})
def encode(self, X):
session = tensorflow_backend._get_session()
return session.run([self.mu, self.log_sigma], feed_dict={self.x: X})
def reconstruct(self, X):
session = tensorflow_backend._get_session()
return session.run([self.x_hat_mu, self.x_hat_log_sigma], feed_dict={self.x: X})
def test_TensorBoard():
import shutil
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
old_session = KTF._get_session()
filepath = './logs'
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples,
nb_test=test_samples,
input_shape=(input_dim,),
classification=True,
nb_class=nb_class)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
def data_generator(train):
if train:
max_batch_index = len(X_train) // batch_size
else:
max_batch_index = len(X_test) // batch_size
i = 0
while 1:
if train:
yield (X_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size])
else:
yield (X_test[i * batch_size: (i + 1) * batch_size], y_test[i * batch_size: (i + 1) * batch_size])
i += 1
i = i % max_batch_index
def data_generator_graph(train):
while 1:
if train:
yield {'X_vars': X_train, 'output': y_train}
else:
yield {'X_vars': X_test, 'output': y_test}
# case 1 Sequential
with tf.Graph().as_default():
session = tf.Session('')
KTF._set_session(session)
model = Sequential()
model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(nb_class, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd')
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
# fit with validation data
model.fit(X_train, y_train, batch_size=batch_size, show_accuracy=False,
validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2)
# fit with validation data and accuracy
model.fit(X_train, y_train, batch_size=batch_size, show_accuracy=True,
validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2)
# fit generator with validation data
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
show_accuracy=False,
validation_data=(X_test, y_test),
callbacks=cbks)
# fit generator without validation data
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
show_accuracy=False,
callbacks=cbks)
# fit generator with validation data and accuracy
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
show_accuracy=True,
validation_data=(X_test, y_test),
callbacks=cbks)
# fit generator without validation data and accuracy
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
show_accuracy=True,
callbacks=cbks)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
# case 2 Graph
with tf.Graph().as_default():
session = tf.Session('')
KTF._set_session(session)
model = Graph()
model.add_input(name='X_vars', input_shape=(input_dim, ))
model.add_node(Dense(nb_hidden, activation="sigmoid"),
name='Dense1', input='X_vars')
model.add_node(Dense(nb_class, activation="softmax"),
name='last_dense',
input='Dense1')
model.add_output(name='output', input='last_dense')
model.compile(optimizer='sgd', loss={'output': 'mse'})
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
# fit with validation
model.fit({'X_vars': X_train, 'output': y_train},
batch_size=batch_size,
validation_data={'X_vars': X_test, 'output': y_test},
callbacks=cbks, nb_epoch=2)
# fit wo validation
model.fit({'X_vars': X_train, 'output': y_train},
batch_size=batch_size,
callbacks=cbks, nb_epoch=2)
# fit generator with validation
model.fit_generator(data_generator_graph(True), 1000, nb_epoch=2,
validation_data={'X_vars': X_test, 'output': y_test},
callbacks=cbks)
# fit generator wo validation
model.fit_generator(data_generator_graph(True), 1000, nb_epoch=2,
callbacks=cbks)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
KTF._set_session(old_session)
