imdb.py

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

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



# decoding algorithm 
word_index = imdb.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])
print(decoded_review)

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:]

model.fit(x_train,y_train,epochs=5,batch_size=512)
results = model.evaluate(x_test,y_test)
prediction = model.predict(x_test)
print(results)
for i in prediction:
	if(i > 0.5):
		print("positive")
	else:
		print("negative")