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from keras.datasets import imdb

from keras.layers import Dense

from keras.layers import Flatten

from keras.layers.convolutional import Convolution1D

from keras.layers.convolutional import MaxPooling1D

from keras.layers.embeddings import Embedding

from keras.models import Sequential

from keras.preprocessing import sequence

import numpy as np

(X_train, y_train), (X_test, y_test) = imdb.load_data()
X = np.concatenate((X_train, X_test), axis=0)
y = np.concatenate((y_train, y_test), axis=0)

top_words = 10000
vector_size = 32
max_review_lenght = 800
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
X_train = sequence.pad_sequences(X_train, maxlen=max_review_lenght)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_lenght)

model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Flatten())
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=128, verbose=1)

scores = model.evaluate(X_test, y_test, verbose=0)

model = Sequential()
model.add(Embedding(top_words, vector_size, input_length=max_review_lenght))
model.add(Convolution1D(activation='relu', filters=vector_size, kernel_size=3, padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(250, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())