imdb_build.py

#-*- coding:utf-8 -*-
#author:zhangwei

import matplotlib.pyplot as plt
import numpy as np
import keras
from keras.utils import np_utils
from keras.datasets import imdb
from keras.models import Sequential , Model
from keras.layers import  Input , Conv2D , MaxPool2D , Dense
from keras.layers import Reshape , Flatten , Activation , Dropout , BatchNormalization
from keras.optimizers import SGD , Adam , RMSprop
from keras import backend as K

(train_images , train_labels) , (test_images , test_labels) = imdb.load_data(num_words=10000)

# for sequence in train_images:
#     print(len(sequence))

# sequence = [max(sequence) for sequence in train_images]

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_images)
x_test = vectorize_sequences(test_images)
x_val = x_train[:10000]
partial_x_train = x_train[10000:]

# print(train_labels.dtype)
y_train = np.asarray(train_labels).astype('float32')
y_test = np.asarray(train_labels).astype('float32')
y_val = y_train[:10000]
partial_y_train = y_train[10000:]
# print(x_val.shape)

model = Sequential()
model.add(Dense(16 , activation='relu' , input_shape=[10000 ,]))
model.add(Dropout(0.2))
model.add(Dense(units=16 , activation='relu'))
model.add(Dense(units=1 , activation='sigmoid'))
# model.summary()
adam = RMSprop(lr=0.01)
model.compile(optimizer=adam ,
              loss='binary_crossentropy' ,
              metrics=['accuracy'])

history = model.fit(partial_x_train ,
          partial_y_train ,
          batch_size=128 ,
          epochs=20 ,
          validation_data=(x_val , y_val))
score = model.evaluate(x_test , y_test)
print("TestACC" , score[1])
predicts = model.predict(x_test)
print(predicts)

history_dict = history.history
acc = history_dict['acc']
val_acc = history_dict['val_acc']
epochs = range(1 , len(acc) + 1)
plt.plot(epochs , acc , 'bo' , label='Traing Accuracy')
plt.plot(epochs , val_acc , 'b' , label='Validation Accuracy')
plt.title('Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()