Deep-Learning-with-Python/imdb_example.py at master · jaydee829/Deep-Learning-with-Python · GitHub

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from keras.datasets import imdb
from keras import models,layers,optimizers
import numpy as np

(train_data, train_labels),(test_data, test_labels)=imdb.load_data(num_words=10000)

def vectorize_sequences(sequences,dimension=10000):
    results=np.zeros((len(sequences),dimension))
    for i, sequence in enumerate(sequences):
        results[i,sequence]=1.
    return results

x_train=vectorize_sequences(train_data)
x_test=vectorize_sequences(test_data)
y_train=np.asarray(train_labels).astype('float32')
y_test=np.asarray(test_labels).astype('float32')

model=models.Sequential()
model.add(layers.Dense(16,activation='relu',input_shape=(10000,)))
model.add(layers.Dense(16,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))

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

x_val=x_train[:10000]
partial_x_train=x_train[10000:]
y_val=y_train[:10000]
partial_y_train=y_train[10000:]

history=model.fit(x_train,y_train,epochs=4,batch_size=512)

results=model.evaluate(x_test,y_test)

print(results)