imbd.py

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

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

# encoding of the lists of indices to turn them into tensors
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_label).astype('float32')

#building the model

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=optimizers.RMSprop(lr=0.001),
            loss=losses.binary_crossentropy,
            metrics=[metrics.binary_accuracy])

#setting a validation set and training the models

x_val = x_train[:1000]
y_val = y_train[:1000]
partial_x_train = x_train[1000:]
partial_y_train = y_train[1000:]

history = model.fit(partial_x_train,partial_y_train,epochs=10,batch_size=512,
                    validation_data=(x_val,y_val))

#plotting results

history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']

epochs = range(1, 10 +1)

plt.plot(epochs,loss_values,'bo',label='Training loss')
plt.plot(epochs,val_loss_values,'b',label='Validation_loss')
#plt.xlabel('Epochs')
#plt.ylabel('Loss')
#plt.legend()
#plt.show()

acc_values = history_dict['binary_accuracy']
val_acc_values = history_dict['val_binary_accuracy']
plt.plot(epochs,acc_values,'ro',label='Traning acc')
plt.plot(epochs,val_acc_values,'r',label='Validation acc')
plt.title('Training and validation accuracy and loss')
plt.xlabel('epochs')
plt.ylabel('Loss / Accuracy')
plt.legend()
plt.show()


#Testing on the test data and showing results

results = model.evaluate(x_test,y_test)
print('Results : ')
print(results)