5-movie.py

# -*- coding: utf-8 -*-
"""
Created on Mon Nov 26 16:34:29 2018

@author: zhengyuv
"""

from keras.datasets import imdb
import numpy as np
from keras import models
from keras import layers
import matplotlib.pyplot as plt

#read dataset
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(path='D:\datasets\imdb.npz', num_words=10000)
#print(train_data[0])
#print(len(train_data))
#print(len(test_data))

#vectorize the data
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 definition
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
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

#split dataset
x_val = x_train[:10000]
x_train = x_train[10000:]
y_val = y_train[:10000]
y_train = y_train[10000:]

#train model
history = model.fit(x_train,
                    y_train,
                    epochs=10,
                    batch_size=512,
                    validation_data=(x_val,y_val))

#plot
history_dict = history.history
loss = history_dict['loss']
val_loss = history_dict['val_loss']
acc = history_dict['acc']
val_acc = history_dict['val_acc']

epochs = range(1, len(loss)+1)

plt.plot(epochs, loss, 'bo', label='training loss')
plt.plot(epochs, val_loss, 'r', label='validation loss')
plt.title('training and validation loss')
plt.xlabel('epochs')
plt.ylabel('loss')
plt.legend()
plt.show()

plt.clf()
plt.plot(epochs, acc,'bo', label='training accuracy')
plt.plot(epochs, val_acc, 'r', label='validation accuracy')
plt.title('training and validation accuracy')
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.legend()
plt.show()