FirstTrial.py

import numpy as np
import tensorflow as tf
from keras.datasets import imdb
from keras import models
from keras import layers
from keras.utils import to_categorical
import matplotlib.pyplot as plt

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

# word_index = imdb.get_word_index()
# reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

# def translate(sequence):
#     return ' '.join([reverse_word_index.get(i - 3, '?') for i in sequence])

def vectorize_sequences(sequences, dimention=10000):
    results = np.zeros((len(sequences), dimention))
    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(units=16, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(units=16, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

# model.compile(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.compile(loss='binary_crossentropy', metrics=['acc'])

history = model.fit(partial_x_train, partial_y_train, epochs=4, batch_size=512, validation_data=(x_val, y_val))

results = model.evaluate(x_test, y_test)

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

# epochs = range(1, len(loss_values) + 1)

# plt.plot(epochs, loss_values, 'bo', label='Training loss')
# plt.plot(epochs, val_loss_values, 'b', label='Validation loss')
# plt.title('Training and validation loss')
# plt.xlabel('Epochs')
# plt.ylabel('Loss')
# plt.legend()

# plt.show()