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

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=1000)

def vectorize_sequences(sequences, dimension=1000):
    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')

print(x_train.shape)
print(y_train.shape)

model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(1000,)))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

# from keras import optimizers
# from keras import losses
# from keras import metrics
# model.compile(optimize=optimizers.RMSprop(lr=0.001), loss=losses.binary_crossentropy, metrics=[metrics.binary_accuracy])

x_val = x_train[:10000]
partial_x_train = x_train[10000:]

y_val = y_train[:10000]
partial_y_train = y_train[10000:]

history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))

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.legend()
plt.show()

results = model.evaluate(x_test, y_test)
predictions = model.predict(x_test)

print(results)
print(predictions)

fceeef2b609090c72e38321369ce7417f29c75fc
<mask token>


def vectorize_sequences(sequences, dimension=1000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.0
    return results


<mask token>

<mask token>


def vectorize_sequences(sequences, dimension=1000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.0
    return results


<mask token>
print(x_train.shape)
print(y_train.shape)
<mask token>
model.add(layers.Dense(16, activation='relu', input_shape=(1000,)))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=[
    'accuracy'])
<mask token>
plt.plot(epochs, loss_values, 'bo', label='Training loss')
plt.plot(epochs, val_loss_values, 'b', label='Validation loss')
plt.legend()
plt.show()
<mask token>
print(results)
print(predictions)

<mask token>
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words
    =1000)


def vectorize_sequences(sequences, dimension=1000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.0
    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')
print(x_train.shape)
print(y_train.shape)
model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(1000,)))
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:]
history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size
    =512, validation_data=(x_val, y_val))
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.legend()
plt.show()
results = model.evaluate(x_test, y_test)
predictions = model.predict(x_test)
print(results)
print(predictions)

import numpy as np
from keras import models
from keras import layers
from keras.datasets import imdb
import matplotlib.pyplot as plt
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words
    =1000)


def vectorize_sequences(sequences, dimension=1000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.0
    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')
print(x_train.shape)
print(y_train.shape)
model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(1000,)))
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:]
history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size
    =512, validation_data=(x_val, y_val))
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.legend()
plt.show()
results = model.evaluate(x_test, y_test)
predictions = model.predict(x_test)
print(results)
print(predictions)

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

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=1000)

def vectorize_sequences(sequences, dimension=1000):
    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')

print(x_train.shape)
print(y_train.shape)

model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(1000,)))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

# from keras import optimizers
# from keras import losses
# from keras import metrics
# model.compile(optimize=optimizers.RMSprop(lr=0.001), loss=losses.binary_crossentropy, metrics=[metrics.binary_accuracy])

x_val = x_train[:10000]
partial_x_train = x_train[10000:]

y_val = y_train[:10000]
partial_y_train = y_train[10000:]

history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))

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.legend()
plt.show()

results = model.evaluate(x_test, y_test)
predictions = model.predict(x_test)

print(results)
print(predictions)
