def getPredictions(x_train, x_test, train, test):
embedding_dim = 200
embedding_matrix = create_embedding_matrix('data\\glove.6B.200d.txt',
tokenizer.word_index,
embedding_dim)
model = Sequential()
model.add(
layers.Embedding(vocab_size,
embedding_dim,
weights=[embedding_matrix],
input_length=maxlen,
trainable=True))
model.add(layers.Conv1D(64, 3, activation='relu'))
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=[f1, 'accuracy'])
#model.summary()
history = model.fit(x_train,
train,
epochs=20,
verbose=False,
validation_data=(x_test, test),
batch_size=10)
val = model.evaluate(x_train, train, verbose=False)
val = model.evaluate(x_test, test, verbose=False)
predictions = model.predict(x_test)
return predictions
def __init__(self, parameters):
super(CNN_MODEL, self).__init__(name='CNN-Model')
# Get the required parameters
vocabulary_size = parameters['vocabulary_size']
#max_length = parameters['max_length']
embedding_dimensions = parameters['embedding_dimensions']
cnn_filters = parameters['cnn_filters']
dnn_units = parameters['dnn_units']
dropout_rate = parameters['dropout_rate']
#kernel_initializer = parameters['kernel_initializer']
self.embedding = layers.Embedding(vocabulary_size,
embedding_dimensions)
self.cnn_layer1 = layers.Conv1D(filters=cnn_filters,
kernel_size=2,
padding="valid",
activation="relu")
self.cnn_layer2 = layers.Conv1D(filters=cnn_filters,
kernel_size=3,
padding="valid",
activation="relu")
self.cnn_layer3 = layers.Conv1D(filters=cnn_filters,
kernel_size=4,
padding="valid",
activation="relu")
self.pool = layers.GlobalMaxPool1D()
self.dense_1 = layers.Dense(units=dnn_units, activation="relu")
self.dropout = layers.Dropout(rate=dropout_rate)
self.last_dense = layers.Dense(units=1, activation="sigmoid")
def DNN_MaxPooling1D(embedding_dim, vocab_size, maxlen):
'''It is a sequential four layer neural network where first layer is an Embedding layer, second layer
is maxpooling layer followed by a dense layer using relu activation and finally the last dense layer using
sigmoid activation function.'''
model = Sequential() # Sequential is a Keras API which allows us to create models layer-by-layer piecewise.
model.add(layers.Embedding(input_dim=vocab_size,
output_dim=embedding_dim,
input_length=maxlen)) # It is the first, embedding layer of the model which turns
# positive integers(indexes) into dense vectors of fixed size. input_dim = vocab_size(4933 in
# this case). Output_dimension = embedding_dim(50 in this case), is the dimension of dense embedding.
# input_length = maxlen(100 in this case), is the length of input sequence.
model.add(layers.GlobalMaxPool1D()) # using maxpooling layer to reduce the spatial size of the representation.
model.add(layers.Dense(10, activation='relu')) # Dense is used to create densely-connected
# Neural Network layers. we have taken units:10, which means output array shape will be(*, 10). input_dim is the
# dimension of input fed to the layer and activation:relu, basically uses rectified linear unit to convert input
# signal into output signal at a A-NN node. It has been seen that it provides better convergence and also it
# also rectifies vanishing gradient problem.
model.add(layers.Dense(1, activation='sigmoid')) # it is the last layer whose output shape array shape
# will be(*, 1). The activation function used is Sigmoid whose range is between 0 and 1.
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
# compile is a method of sequential class which configures the model for training. We are using binary_crossentropy
# loss function to calculate loss, and adam optimizer. we want to evaluate just the accuracy metric.
model.summary() # prints a summary representation of the model.
return model
def createRCNN(word_index, embedding_matrix):
input_layer = layers.Input((1000, ))
embedding_layer = layers.Embedding(len(word_index) + 1,
300,
weights=[embedding_matrix],
trainable=False)(input_layer)
embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer)
rnn_layer = layers.Bidirectional(layers.GRU(
50, return_sequences=True))(embedding_layer)
conv_layer = layers.Convolution1D(100, 3, activation="relu")(rnn_layer)
pooling_layer = layers.GlobalMaxPool1D()(conv_layer)
output_layer1 = layers.Dense(50, activation="relu")(pooling_layer)
output_layer1 = layers.Dropout(0.25)(output_layer1)
output_layer2 = layers.Dense(1, activation="sigmoid")(output_layer1)
model = models.Model(inputs=input_layer, outputs=output_layer2)
model.compile(optimizer=optimizers.Adam(), loss='binary_crossentropy')
return model
def create_cnn(inp_size):
# Add an Input Layer
input_layer = layers.Input((inp_size, ))
# embedding layer learnt from above
embedding_layer = layers.Embedding(vocab_size, 200)(input_layer)
# add dropout on this layer
embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer)
# Add the convolutional Layer
conv_layer = layers.Convolution1D(100, 3,
activation="tanh")(embedding_layer)
# Add the pooling Layer
pooling_layer = layers.GlobalMaxPool1D()(conv_layer)
# Add the output Layers
output_layer1 = layers.Dense(50, activation="relu")(pooling_layer)
output_layer1 = layers.Dropout(0.25)(output_layer1)
output_layer2 = layers.Dense(1, activation="sigmoid")(output_layer1)
# Compile the model
model = models.Model(inputs=input_layer, outputs=output_layer2)
model.compile(optimizer=optimizers.Adam(),
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def cnn(xtrain, ytrain, xvalid, yvalid, epochs=3):
# Add an Input Layer
input_layer = layers.Input((70, ))
# Add the word embedding Layer
embedding_layer = layers.Embedding(len(word_index) + 1,
300,
weights=[embedding_matrix],
trainable=False)(input_layer)
embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer)
# Add the convolutional Layer
conv_layer = layers.Convolution1D(100, 4,
activation="relu")(embedding_layer)
# Add the pooling Layer
pooling_layer = layers.GlobalMaxPool1D()(conv_layer)
# Add the output Layers
output_layer1 = layers.Dense(50, activation="relu")(pooling_layer)
output_layer1 = layers.Dropout(0.25)(output_layer1)
output_layer2 = layers.Dense(4, activation="softmax")(output_layer1)
# Compile the model
model = models.Model(inputs=input_layer, outputs=output_layer2)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(xtrain, ytrain, batch_size=256, epochs=epochs)
predictions = model.predict(xvalid)
predictions = predictions.argmax(axis=-1)
accuracy = model.evaluate(xvalid, yvalid, verbose=0)
f1score = metrics.f1_score(valid_y, predictions, average='weighted')
return accuracy, f1score
def create_rcnn():
# Add an Input Layer
input_layer = layers.Input((70, ))
# Add the word embedding Layer
embedding_layer = layers.Embedding(len(word_index) + 1, 300, weights=[embedding_matrix], trainable=False)(input_layer)
embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer)
# Add the recurrent layer
layers.Bidirectional(layers.GRU(50, return_sequences=True))(embedding_layer)
# Add the convolutional Layer
conv_layer = layers.Convolution1D(100, 3, activation="relu")(embedding_layer)
# Add the pooling Layer
pooling_layer = layers.GlobalMaxPool1D()(conv_layer)
# Add the output Layers
output_layer1 = layers.Dense(50, activation="relu")(pooling_layer)
output_layer1 = layers.Dropout(0.25)(output_layer1)
output_layer2 = layers.Dense(units=max(training_label_encoded) + 1, activation="softmax", name="ouput_layer")(output_layer1)
# Compile the model
model = models.Model(inputs=input_layer, outputs=output_layer2)
model.compile(optimizer=optimizers.Adam(), loss='sparse_categorical_crossentropy', metrics=["sparse_categorical_accuracy"])
return model
def create_cnn():
# Add an Input Layer
input_layer = layers.Input((70, ))
# Add the word embedding Layer
embedding_layer = layers.Embedding(len(word_index) + 1,
50,
weights=[embedding_matrix],
trainable=False)(input_layer)
embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer)
# Add the convolutional Layer
conv_layer = layers.Convolution1D(100, 3,
activation="relu")(embedding_layer)
# Add the pooling Layer
pooling_layer = layers.GlobalMaxPool1D()(conv_layer)
# Add the output Layers
output_layer1 = layers.Dense(50, activation="relu")(pooling_layer)
output_layer1 = layers.Dropout(0.25)(output_layer1)
output_layer2 = layers.Dense(1, activation="sigmoid")(output_layer1)
# Compile the model
model = models.Model(inputs=input_layer, outputs=output_layer2)
model.compile(optimizer=optimizers.Adam(), loss='binary_crossentropy')
return model
def create_cnn():
# Add an Input Layer
input_layer = layers.Input((100, ))
# Add the word embedding Layer
embedding_layer = layers.Embedding(10000, 100, trainable=True)(input_layer)
embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer)
# Add the convolutional Layer
conv_layer = layers.Conv1D(100,
3,
padding='valid',
activation="relu",
strides=1)(embedding_layer)
# Add the pooling Layer
pooling_layer = layers.GlobalMaxPool1D()(conv_layer)
# Add the output Layers
output_layer1 = layers.Dense(1, activation="sigmoid")(pooling_layer)
# Compile the model
model = models.Model(inputs=input_layer, outputs=output_layer1)
model.compile(optimizer=optimizers.Adam(),
loss='binary_crossentropy',
metrics=['accuracy'])
model.summary()
return model
def model_3(self):
embedding_matrix = self.build_myself_embedding_matrix()
input = layers.Input(shape=(self.max_words,))
embedding = layers.Embedding(input_dim=embedding_matrix.shape[0], output_dim=embedding_matrix.shape[1],
input_length=self.max_words, weights=[embedding_matrix])
x = layers.SpatialDropout1D(0.2)(embedding(input))
x = layers.Bidirectional(layers.GRU(400, return_sequences=True))(x)
x = layers.Bidirectional(layers.GRU(400, return_sequences=True))(x)
avg_pool = layers.GlobalAveragePooling1D()(x)
max_pool = layers.GlobalMaxPool1D()(x)
concat = layers.concatenate([avg_pool, max_pool])
x = layers.Dense(1024)(concat)
x = layers.BatchNormalization()(x)
x = layers.Activation(activation='relu')(x)
x = layers.Dropout(0.2)(x)
x = layers.Dense(512)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation(activation='relu')(x)
x = layers.Dropout(0.2)(x)
output = layers.Dense(self.class_num, activation='softmax')(x)
model = models.Model(input=input, output=output)
print(model.summary())
return model
def trainDeepModelOnEmbeddings(self, X_train, y_train, X_test, y_test):
model_eval = ModelEvaluation()
embedding_dim = 50
input_dim = X_train.shape[1]
model = models.Sequential()
model.add(
layers.Embedding(input_dim=input_dim,
output_dim=embedding_dim,
input_length=None))
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.summary()
history = model.fit(X_train,
y_train,
epochs=1,
verbose=False,
validation_data=(X_test, y_test),
batch_size=10)
loss, accuracy = model.evaluate(X_train, y_train, verbose=False)
print("Training Accuracy: {:.4f}".format(accuracy))
loss, accuracy = model.evaluate(X_test, y_test, verbose=False)
print("Testing Accuracy: {:.4f}".format(accuracy))
model_eval.plot_history(history)
return True
def black_box(vocab_size, embedding_dim, embedding_matrix, maxlen):
"""
Return a Keras fast-forward NN model with a embedding layer and two dense layers
"""
model = Sequential()
if embedding_matrix is not None:
model.add(
layers.Embedding(input_dim=vocab_size,
output_dim=embedding_dim,
weights=[embedding_matrix],
input_length=maxlen,
trainable=trainable), )
else:
model.add(
layers.Embedding(input_dim=vocab_size,
output_dim=embedding_dim,
input_length=maxlen,
trainable=trainable), )
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def build_text_cnn_model(vocabulary_size=None,
n_classes=None,
embedding_size=300,
max_len=None,
hidden_size=128,
conv_kernels=None,
name=None):
"""Generate Text-CNN model for text classification
"""
assert vocabulary_size is not None
assert n_classes is not None
if not conv_kernels:
conv_kernels = [2, 3, 4]
input_ = layers.Input(shape=(max_len, ))
embedding = layers.Embedding(vocabulary_size, embedding_size)(input_)
x = embedding
multi_convs = []
for kernel_size in conv_kernels:
conv = layers.Conv1D(hidden_size, kernel_size, activation='relu')(x)
conv = layers.GlobalMaxPool1D()(conv)
multi_convs.append(conv)
x = layers.Concatenate()(multi_convs)
x = layers.Dense(n_classes, activation='softmax')(x)
output = x
model = Model(inputs=input_, outputs=output, name=name)
return model
def myModel(vocab_size, embedding_dim, maxlen, embedding_matrix, X_train,
labels_train, X_test, labels_test):
model2 = Sequential()
model2.add(layers.InputLayer(input_shape=(100, )))
#taking advantage of the Embedding layer of keras
#to initialize an embedding layer, three parameters are requires: input_dim which is the vocab size of the text data
#output_dim which is the size of the output vectors for each word
#input_length which is the length of the input sequences(sentences)
#since i'm using GloVe, we'll use their pretrained weights and allow them to be trained to gain higher accuracy
model2.add(
layers.Embedding(input_dim=vocab_size,
output_dim=embedding_dim,
input_length=maxlen,
weights=[embedding_matrix],
trainable=True))
model2.add(layers.GlobalMaxPool1D())
model2.add(layers.Dense(10, activation='relu'))
model2.add(layers.Dense(1, activation='sigmoid'))
model2.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model2.save('model.h5')
history = model2.fit(X_train,
labels_train,
epochs=100,
verbose=False,
validation_data=(X_test, labels_test),
batch_size=10)
model2.summary()
loss, accuracy = model2.evaluate(X_train, labels_train, verbose=False)
print("Training Accuracy: {:.4f}".format(accuracy))
loss, accuracy = model2.evaluate(X_test, labels_test, verbose=False)
print("Testing Accuracy: {:.4f}".format(accuracy))
def DNN_PretrainedWordEmbeddingGlove(embedding_dim, vocab_size, maxlen, embedding_matrix):
'''It is a sequential four layer neural network where first layer is an Embedding layer which gets weights from
pretrained GloVe word-embeddings, second layer is maxpooling layer followed by a dense layer using relu activation
and finally the last dense layer using sigmoid activation function.'''
model = Sequential() # Sequential is a Keras API which allows us to create models layer-by-layer piecewise.
# TODO: vary trainable between True and False to additionally train the word embeddings.
model.add(layers.Embedding(vocab_size, embedding_dim,
weights=[embedding_matrix],
input_length=maxlen,
trainable=True))# It is the first, embedding layer of the model which uses pretrained
# GloVe word embeddings. We can choose to train the word embeddings additionally by setting trainable=True.
# input_length = maxlen(100 in this case), is the length of input sequence.
model.add(layers.GlobalMaxPool1D()) # using maxpooling layer to reduce the spatial size of the representation.
model.add(layers.Dense(10, activation='relu')) # Dense is used to create densely-connected
# Neural Network layers. we have taken units:10, which means output array shape will be(*, 10). input_dim is the
# dimension of input fed to the layer and activation:relu, basically uses rectified linear unit to convert input
# signal into output signal at a A-NN node. It has been seen that it provides better convergence and also it
# also rectifies vanishing gradient problem.
model.add(layers.Dense(1, activation='sigmoid')) # it is the last layer whose output shape array shape
# will be(*, 1). The activation function used is Sigmoid whose range is between 0 and 1.
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
# compile is a method of sequential class which configures the model for training. We are using binary_crossentropy
# loss function to calculate loss, and adam optimizer. we want to evaluate just the accuracy metric.
model.summary() # prints a summary representation of the model.
return model
def create_cnn():
# Adicione uma camada de entrada
input_layer = layers.Input((70, ))
# Adicione a camada de incorporação de palavras
embedding_layer = layers.Embedding(len(word_index) + 1,
300,
weights=[embedding_matrix],
trainable=False)(input_layer)
embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer)
# Adicione a camada convolucional
conv_layer = layers.Convolution1D(90, 3,
activation="relu")(embedding_layer)
# Adicione a camada de pooling máximo, pega maior valor resltande do mapa de ativação.
pooling_layer = layers.GlobalMaxPool1D()(conv_layer)
# Adicione as camadas de saÃda
# camada totalmente conectada para normalização dos dados.
output_layer1 = layers.Dropout(0.7)(pooling_layer)
output_layer2 = layers.Dense(1, activation="sigmoid")(output_layer1)
# Compile o modelo
model = models.Model(inputs=input_layer, outputs=output_layer2)
model.compile(optimizer=optimizers.Adamax(),
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def char_embedding_graph(input_dim,
output_dim,
cf,
dropout_rate=0.0,
regularizer=None,
name='c_emb'):
layers = KM.Sequential(name=name)
layers.add(
KL.Embedding(input_dim=input_dim,
output_dim=output_dim,
embeddings_initializer=init_embedding_weights(
None, dropout_rate),
trainable=True,
embeddings_regularizer=regularizer,
name=name + '_emb'))
layers.add(
KL.Lambda(lambda x: tf.reshape(x, (-1, cf.WORD_LEN, output_dim)),
name=name + '_reshape'))
layers.add(
KL.Conv1D(cf.C_CONV_FILTERS,
cf.C_CONV_KERNEL,
activation='relu',
kernel_regularizer=regularizer,
name=name + '_conv1d'))
layers.add(KL.GlobalMaxPool1D(name=name + '_pool1d'))
return layers
def train_model(self):
if len(self.X_train) == 0:
self.load_data()
print("Training model...")
self.model = Sequential()
self.model.add(
layers.Embedding(self.config["vocab_size"],
50,
input_length=self.config["max_len"]))
self.model.add(layers.Conv1D(128, 5, activation='relu'))
self.model.add(layers.GlobalMaxPool1D())
self.model.add(layers.Dense(10, activation='relu')
) # 4340*10= 43400 weights + 10 bias = 43410 params
self.model.add(layers.Dense(3, activation='softmax'))
self.model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
self.model.summary()
self.model.fit(self.X_train,
self.y_train,
epochs=100,
verbose=False,
validation_data=(self.X_test, self.y_test),
batch_size=15)
self.model.save("data/models/cnn.h5")
print("Done")
return self.model
def build_model(tokens, categorical_vectorizer, hid_size=64):
""" Build a model that maps three data sources to a single linear output: predicted log1p(salary) """
n_cat_features = len(categorical_vectorizer.vocabulary_)
n_tokens = len(tokens)
l_title = L.Input(shape=[None], name="Title")
l_descr = L.Input(shape=[None], name="FullDescription")
l_categ = L.Input(shape=[n_cat_features], name="Categorical")
# Build your monster!
# <YOUR CODE>
# Embedding (size_vocabulary,embeding_dim,max_length)
embedding_dim = 64
vocabulary_size = n_tokens
num_filters = 32
max_length = 10
nb_filter = 16
# embedding layer
title_embedding = L.Embedding(input_dim=n_tokens,
output_dim=embedding_dim)(l_title)
descr_embedding = L.Embedding(input_dim=n_tokens,
output_dim=embedding_dim)(l_descr)
# convLayer
title_conv = L.Conv1D(nb_filter, max_length,
activation='relu')(title_embedding)
descr_conv = L.Conv1D(nb_filter, max_length,
activation='relu')(descr_embedding)
# MaxPooling Layer
title_pool = L.GlobalMaxPool1D()(title_conv)
descr_pool = L.GlobalMaxPool1D()(descr_conv)
# output_layer_title = L.Conv1D(1,kernel_size = 3)(l_title)
output_layer_categ = L.Dense(hid_size)(l_categ)
concat = L.Concatenate(axis=-1)(
[title_pool, descr_pool, output_layer_categ])
# output_layer2 = L.Dense(hid_size)(output_layer_categ)
output_layer3 = L.Dense(1)(concat)
# end of your code
model = keras.models.Model(inputs=[l_title, l_descr, l_categ],
outputs=[output_layer3])
model.compile('adam',
'mean_squared_error',
metrics=['mean_absolute_error'])
return model
def build_model(emb_cid, emb_advid):
inp1 = layers.Input(shape=(max_len, ))
inp2 = layers.Input(shape=(max_len, ))
emb1 = layers.Embedding(input_dim=emb_cid.shape[0],
output_dim=emb_cid.shape[1],
input_length=max_len,
weights=[emb_cid],
trainable=False)(inp1)
emb2 = layers.Embedding(input_dim=emb_advid.shape[0],
output_dim=emb_advid.shape[1],
input_length=max_len,
weights=[emb_advid],
trainable=False)(inp2)
sdrop = layers.SpatialDropout1D(rate=0.2)
emb1 = sdrop(emb1)
emb2 = sdrop(emb2)
content = layers.Concatenate()([emb1, emb2])
mha = MultiHeadAttention(head_num=16)(content)
mha = layers.Dropout(0.01)(mha)
mha = layers.Add()([content, mha])
mha = LayerNormalization()(mha)
mha = layers.Dropout(0.01)(mha)
mha_ff = FeedForward(256)(mha)
mha_out = layers.Add()([mha, mha_ff])
mha_out = LayerNormalization()(mha_out)
lstm = layers.Bidirectional(layers.LSTM(128,
return_sequences=True))(mha_out)
avg_pool = layers.GlobalAveragePooling1D()(lstm)
max_pool = layers.GlobalMaxPool1D()(lstm)
x = layers.Concatenate()([avg_pool, max_pool])
x = layers.Dense(128, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.Dense(64, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.1)(x)
out = layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs=[inp1, inp2], outputs=out)
model.compile(loss='categorical_crossentropy',
optimizer=keras.optimizers.Adam(1e-3),
metrics=['accuracy'])
return model
def create_cnn():
input_layer = layers.Input((70,))
embedding_layer = layers.Embedding(len(word_index)+1, 300, weights=[embedding_matrix], trainable=False)(input_layer)
embedding_layer = layers.SpatialDropout1D(0.3)(embedding_layer)
conv_layer = layers.Convolution1D(100, 3, activation='relu')(embedding_layer)
pooling_layer = layers.GlobalMaxPool1D()(conv_layer)
output_layer1 = layers.Dense(50, activation='relu')(pooling_layer)
output_layer1 = layers.Dropout(0.25)(output_layer1)
output_layer2 = layers.Dense(1, activation='sigmoid')(output_layer1)
model = models.Model(inputs=input_layer, outputs=output_layer2)
model.compile(optimizer=optimizers.Adam(), loss='binary_crossentropy')
return model
def multipleOutput(vocabulary_size, num_income_groups):
"""
multiple output model
:param vocabulary_size:
:param num_income_groups:
:return:
"""
post_input = layers.Input(shape=(None, ), dtype='int32', name='posts')
embedded_post = layers.Embedding(input_dim=256,
output_dim=vocabulary_size)(post_input)
# Conv1D layer
x = layers.Conv1D(filters=128, kernel_size=5,
activation='relu')(embedded_post)
x = layers.MaxPooling1D(pool_size=5)(x)
x = layers.Conv1D(filters=256, kernel_size=5, activation='relu')(x)
x = layers.Conv1D(filters=256, kernel_size=5, activation='relu')(x)
x = layers.MaxPooling1D(pool_size=5)(x)
x = layers.Conv1D(filters=256, kernel_size=5, activation='relu')(x)
x = layers.Conv1D(filters=256, kernel_size=5, activation='relu')(x)
x = layers.GlobalMaxPool1D()(x)
# Dense layer
# regression problem
age_prediction = layers.Dense(units=1, name='age')(x)
# category category problem
income_prediction = layers.Dense(units=num_income_groups,
activation='softmax',
name='income')(x)
# binary category
gender_prediction = layers.Dense(units=1,
activation='sigmoid',
name='gender')(x)
# model
model = Model(
inputs=post_input,
outputs=[age_prediction, income_prediction, gender_prediction])
model.compile(optimizer=RMSprop(lr=1e-5),
loss={
'age': 'mse',
'income': 'categorical_crossentropy',
'gender': 'binary_crossentropy'
},
loss_weights={
'age': 0.25,
'income': 1.,
'gender': 10.
})
return model
def create_model(num_filters, kernel_size, vocab_size, embedding_dim, maxlen):
model = Sequential()
model.add(layers.Embedding(vocab_size, embedding_dim, input_length=maxlen))
model.add(layers.SpatialDropout1D(0.25))
model.add(layers.Conv1D(num_filters, kernel_size, padding='same', activation='relu'))
model.add(layers.AveragePooling1D())
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dropout(0.25))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def create_model_cnn(n_salida, n_oculto, em_input, em_dim, em_maxlen):
from keras import layers
model = Sequential()
model.add(
layers.Embedding(input_dim=em_input,
output_dim=em_dim,
input_length=em_maxlen))
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dense(n_oculto, activation='relu'))
model.add(layers.Dense(units=n_salida, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def get_baseline_convolutional_encoder(filters,
embedding_dimension,
input_shape=None,
dropout=0.05):
encoder = Sequential()
# Initial conv
if input_shape is None:
# In this case we are using the encoder as part of a siamese network and the input shape will be determined
# automatically based on the input shape of the siamese network
encoder.add(
layers.Conv1D(filters, 32, padding='same', activation='relu'))
else:
# In this case we are using the encoder to build a classifier network and the input shape must be defined
encoder.add(
layers.Conv1D(filters,
32,
padding='same',
activation='relu',
input_shape=input_shape))
encoder.add(layers.BatchNormalization())
encoder.add(layers.SpatialDropout1D(dropout))
encoder.add(layers.MaxPool1D(4, 4))
# Further convs
encoder.add(
layers.Conv1D(2 * filters, 3, padding='same', activation='relu'))
encoder.add(layers.BatchNormalization())
encoder.add(layers.SpatialDropout1D(dropout))
encoder.add(layers.MaxPool1D())
encoder.add(
layers.Conv1D(3 * filters, 3, padding='same', activation='relu'))
encoder.add(layers.BatchNormalization())
encoder.add(layers.SpatialDropout1D(dropout))
encoder.add(layers.MaxPool1D())
encoder.add(
layers.Conv1D(4 * filters, 3, padding='same', activation='relu'))
encoder.add(layers.BatchNormalization())
encoder.add(layers.SpatialDropout1D(dropout))
encoder.add(layers.MaxPool1D())
encoder.add(layers.GlobalMaxPool1D())
encoder.add(layers.Dense(embedding_dimension))
return encoder
def char_level_token_encoder(self):
charset_size = self.parameters.get('charset_size')
char_embedding_size = self.parameters.get('char_embedding_size')
token_embedding_size = self.parameters.get('hidden_units_size')
n_highway_layers = self.parameters.get('n_highway_layers')
filters = self.parameters.get('cnn_filters')
token_maxlen = self.parameters.get('token_maxlen')
# Input Layer, word characters (samples, words, character_indices)
inputs = layers.Input(shape=(
None,
token_maxlen,
), dtype='int32')
# Embed characters (samples, words, characters, character embedding)
embedding = layers.Embedding(input_dim=charset_size,
output_dim=char_embedding_size)(inputs)
token_embeds = []
# Apply multi-filter 2D convolutions + 1D MaxPooling + tanh
for (window_size, filters_size) in filters:
convs = layers.Conv2D(filters=filters_size,
kernel_size=(window_size,
char_embedding_size),
strides=(1, 1),
padding='same')(embedding)
convs = layers.TimeDistributed(layers.GlobalMaxPool1D())(convs)
convs = layers.Activation(activation='tanh')(convs)
convs = Camouflage(mask_value=0)(inputs=[convs, inputs])
token_embeds.append(convs)
token_embeds = layers.concatenate(token_embeds)
# Apply highways networks
for i in range(n_highway_layers):
token_embeds = layers.TimeDistributed(HighWay())(token_embeds)
token_embeds = Camouflage(mask_value=0)(
input=[token_embeds, inputs])
# Project to token embedding dimensionality
token_embeds = layers.TimeDistributed(
layers.Dense(units=token_embedding_size,
activation='linear'))(token_embeds)
token_embeds = Camouflage(mask_value=0)(input=[token_embeds, inputs])
token_encoder = models.Model(inputs=inputs,
outputs=token_embeds,
name='token_encoding')
return token_encoder
def create_model(self, num_filters, kernel_size, vocab_size, embedding_dim,
maxlen):
#creates a CNN model with the specified params, returns the model oect itself
model = Sequential()
model.add(
layers.Embedding(input_dim=vocab_size,
output_dim=embedding_dim,
input_length=maxlen))
model.add(layers.Conv1D(num_filters, kernel_size, activation='relu'))
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.summary()
return model
def createModel2D(self, train_shape, output_count):
model = Sequential()
model.add(
Dense(self.count_first_layer,
input_shape=(
train_shape[0],
train_shape[1],
),
activation="relu"))
model.add(Dense(self.count_second_layer, activation="relu"))
model.add(layers.GlobalMaxPool1D())
model.add(Dense(self.count_third_layer, activation=tf.nn.leaky_relu))
model.add(Dense(output_count, activation="softmax"))
model.compile(loss='categorical_crossentropy',
metrics=['accuracy'],
optimizer='adam')
return model
def train(self, X_train, y_train, X_test, y_test, embedding_matrix,
vocab_size):
pool = []
inputs = []
for kernel_size, n_filters in zip(self.kernel_sizes, self.n_filters):
input_shape = (self.text_length, self.embedding_dim)
x = Input(shape=(self.text_length, ))
embedding = layers.Embedding(vocab_size,
self.embedding_dim,
input_length=self.text_length)(x)
C1D = layers.Conv1D(n_filters,
kernel_size,
activation='relu',
input_shape=(self.text_length,
self.embedding_dim))(embedding)
MX1D = layers.GlobalMaxPool1D()(C1D)
pool.append(MX1D)
inputs.append(x)
merged = pool[0]
if len(self.kernel_sizes) > 1:
merged = concatenate(pool)
if len(self.hidden_layers) > 0:
dense = layers.Dense(self.hidden_layers[0],
activation='relu')(merged)
for n_unit in self.hidden_layers[1:]:
dense = layers.Dense(n_unit, activation='relu')(dense)
#dense2 = layers.Dense(10, activation='relu')(dense1)
outputs = layers.Dense(self.n_class, activation='softmax')(dense)
else:
outputs = layers.Dense(self.n_class, activation='softmax')(merged)
self.model = Model(inputs=inputs, outputs=outputs)
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.1, nesterov=True)
self.model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
self.model.summary()
history = self.model.fit(
[X_train] * len(self.kernel_sizes),
y_train,
epochs=self.epochs,
verbose=True,
validation_data=([X_test] * len(self.kernel_sizes), y_test),
batch_size=self.batch_size)
return self.model
def build_keras_model_mv03r00(emb_dims, vocab_size, max_len, emb_matrix):
ip = kl.Input(shape=(max_len, ))
x = kl.Embedding(vocab_size,
emb_dims,
weights=[emb_matrix],
trainable=True,
name='X_emb')(ip)
x = kl.SpatialDropout1D(0.5)(x)
x = kl.Bidirectional(kl.CuDNNGRU(200, return_sequences=True))(x)
x = kl.Bidirectional(kl.CuDNNGRU(200, return_sequences=True))(x)
x = kl.GlobalMaxPool1D()(x)
x = kl.Dense(100, activation="relu")(x)
x = kl.Dropout(0.5)(x)
op = kl.Dense(6, activation="sigmoid")(x)
model = km.Model(inputs=[ip], outputs=op)
return model