from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Embedding, Conv1D, MaxPooling1D, GlobalMaxPooling1D, Dense

from keras.optimizers import RMSprop

max_features = 10000
max_len = 500

(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words = max_features)
 

print(len(input_train), 'train sequences')
print(input_train[0])

model = Sequential()
model.add(Embedding(max_features, 128, input_length = max_len))
model.add(Conv1D(32, 7, activation = 'relu'))
model.add(MaxPooling1D(5))
model.add(Conv1D(32, 7, activation = 'relu'))
model.add(GlobalMaxPooling1D()
 
)
model.add(Dense(1))

model.summary()

model.compile(optimizer = RMSprop(lr = 1e-4),
             loss = 'binary_crossentropy',
             metrics = ['acc'])

history = model.fit(x_train, y_train,
                   epochs = 10,
                   batch_size = 128,
                   validation_split =
 
 0.2)