Add primitive for Sequence classification with 1D convolutions

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@Hector-hedb12 is already working on this.

Since Apr 4, 2019.

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Description

approved new primitives

Related to #121

The architecture would be:

Conv1D (relu) ---> Conv1D (relu) --> MaxPooling1D --> 
  Conv1D (relu) ---> Conv1D (relu) --> GlobalAveragePooling1D --> Dropout -->
    Dense (sigmoid)

You can find an example of this here in the Sequence classification with 1D convolutions section:

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D

seq_length = 64

model = Sequential()
model.add(Conv1D(64, 3, activation='relu', input_shape=(seq_length, 100)))
model.add(Conv1D(64, 3, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

model.fit(x_train, y_train, batch_size=16, epochs=10)
score = model.evaluate(x_test, y_test, batch_size=16)
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