def prep_model(self, module_prep_model, oact='sigmoid'):
# Input embedding and encoding
model = Sequential()
N = B.embedding(model,
self.emb,
self.vocab,
self.s0pad,
self.s1pad,
self.c['inp_e_dropout'],
self.c['inp_w_dropout'],
add_flags=self.c['e_add_flags'])
# Sentence-aggregate embeddings
final_outputs = module_prep_model(model, N, self.s0pad, self.s1pad,
self.c)
# Measurement
if self.c['ptscorer'] == '1':
# special scoring mode just based on the answer
# (assuming that the question match is carried over to the answer
# via attention or another mechanism)
ptscorer = B.cat_ptscorer
final_outputs = [final_outputs[1]]
else:
ptscorer = self.c['ptscorer']
kwargs = dict()
if ptscorer == B.mlp_ptscorer:
kwargs['sum_mode'] = self.c['mlpsum']
kwargs['Dinit'] = self.c['Dinit']
if 'f_add' in self.c:
for inp in self.c['f_add']:
model.add_input(inp, input_shape=(1, )) # assumed scalar
kwargs['extra_inp'] = self.c['f_add']
model.add_node(name='scoreS',
input=ptscorer(model, final_outputs, self.c['Ddim'], N,
self.c['l2reg'], **kwargs),
layer=Activation(oact))
model.add_output(name='score', input='scoreS')
return model
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 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',
metrics=['accuracy'])
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,
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,
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,
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,
callbacks=cbks)
# fit generator with validation data and accuracy
model.fit_generator(data_generator(True),
len(X_train),
nb_epoch=2,
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,
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)
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',
metrics=['accuracy'])
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,
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,
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,
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,
callbacks=cbks)
# fit generator with validation data and accuracy
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
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,
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)
# Layer 8
model.add(Dense(10, init='glorot_normal'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer=RMSprop())
model.fit(X_train[:10],
Y_train[:10],
batch_size=1,
nb_epoch=1,
verbose=1,
show_accuracy=True)
# V. GoogleNet Model
model = Graph()
model.add_input(name='n00', input_shape=(1, 28, 28))
# layer 1
model.add_node(Convolution2D(64, 1, 1, activation='relu'),
name='n11',
input='n00')
model.add_node(Flatten(), name='n11_f', input='n11')
model.add_node(Convolution2D(96, 1, 1, activation='relu'),
name='n12',
input='n00')
model.add_node(Convolution2D(16, 1, 1, activation='relu'),
name='n13',
input='n00')
except: embedding_weights[index,:] = np.random.rand( 1 , embeddings_dim )
le = preprocessing.LabelEncoder( )
le.fit( train_labels + test_labels )
train_labels = le.transform( train_labels )
test_labels = le.transform( test_labels )
print "Classes that are considered in the problem : " + repr( le.classes_ )
print ("Method = CNN from the paper 'Convolutional Neural Networks for Sentence Classification'")
np.random.seed(0)
nb_filter = embeddings_dim
model = Graph()
model.add_input(name='input', input_shape=(max_sent_len,), dtype=int)
model.add_node(Embedding(max_features, embeddings_dim, input_length=max_sent_len, mask_zero=False, weights=[embedding_weights] ), name='embedding', input='input')
model.add_node(Dropout(0.25), name='dropout_embedding', input='embedding')
for n_gram in [3, 5, 7]:
model.add_node(Convolution1D(nb_filter=nb_filter, filter_length=n_gram, border_mode='valid', activation='relu', subsample_length=1, input_dim=embeddings_dim, input_length=max_sent_len), name='conv_' + str(n_gram), input='dropout_embedding')
model.add_node(MaxPooling1D(pool_length=max_sent_len - n_gram + 1), name='maxpool_' + str(n_gram), input='conv_' + str(n_gram))
model.add_node(Flatten(), name='flat_' + str(n_gram), input='maxpool_' + str(n_gram))
model.add_node(Dropout(0.25), name='dropout', inputs=['flat_' + str(n) for n in [3, 5, 7]])
model.add_node(Dense(1, input_dim=nb_filter * len([3, 5, 7])), name='dense', input='dropout')
model.add_node(Activation('sigmoid'), name='sigmoid', input='dense')
model.add_output(name='output', input='sigmoid')
if num_classes == 2: model.compile(loss={'output': 'binary_crossentropy'}, optimizer='adam')
else: model.compile(loss={'output': 'categorical_crossentropy'}, optimizer='adam')
model.fit({'input': train_sequences, 'output': train_labels}, batch_size=32, nb_epoch=30)
results = np.array(model.predict({'input': test_sequences}, batch_size=32)['output'])
if num_classes != 2: results = results.argmax(axis=-1)