import MSAI_NLP

import keras

print('Using Keras', keras.__version__)

import numpy as np

import matplotlib.pyplot as plt

from keras import layers

from keras import optimizers

from keras import losses

from keras import metrics

from keras.layers import Dropout, Dense

from keras.models import Sequential

from keras.datasets import imdb

def p(mess, obj):

print(mess, type(obj), "\n", obj)

lexicon_size = 20000

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

word_index = imdb.get_word_index()

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

reviews = {}

titles = {}

for i, encoded_review in enumerate(train_data):

decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in encoded_review])

reviews[i] = decoded_review

titles[i] = decoded_review[:20]

for i, title in enumerate(reviews):

print(reviews[title])

print('---------')

if i > 20:

break

msai_nlp = MSAI_NLP.MSAI_NLP()

msai_nlp.feed_corpus(reviews)

print('train_data len', len(train_data))

print('train_labels len', len(train_labels))

print('reviews len', len(reviews))

print('tfs_mat len', len(msai_nlp.tfs_mat))

model = Sequential()

model.add(Dense(64, activation='relu', input_shape=(msai_nlp.lexicon_size,)))

model.add(Dropout(0.4))

model.add(Dense(64, activation='relu'))

model.add(Dropout(0.4))

model.add(layers.Dense(1, activation='sigmoid'))

print(model.summary())

model.compile(optimizer=optimizers.RMSprop(lr=0.001),

loss=losses.binary_crossentropy,

metrics=[metrics.binary_accuracy])

np.random.seed(17)

rand_sample_index = np.random.randint(len(train_data))

print(train_labels[rand_sample_index])

y_train = np.asarray(train_labels).astype('float32')

print(y_train[rand_sample_index])

y_test = np.asarray(test_labels).astype('float32')

x_val = msai_nlp.tfs_mat[:10000]

partial_x_train = msai_nlp.tfs_mat[10000:]

y_val = y_train[:10000]

partial_y_train = y_train[10000:]

history = model.fit(partial_x_train,

validation_data=(x_val, y_val))

history_dict = history.history

print(history_dict.keys())

acc = history.history['binary_accuracy']

val_acc = history.history['val_binary_accuracy']

loss = history.history['loss']

val_loss = history.history['val_loss']

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

# "bo" is for "blue dot"

plt.plot(epochs, loss, 'bo', label='Training loss')

# b is for "solid blue line"

plt.plot(epochs, val_loss, 'b', label='Validation loss')

plt.title('Training and validation loss')

plt.xlabel('Epochs')

plt.ylabel('Loss')

plt.legend()

plt.show()

# "bo" is for "blue dot"

plt.plot(epochs, acc, 'bo', label='Training accuracy')

# b is for "solid blue line"

plt.plot(epochs, val_acc, 'b', label='Validation accuracy')

plt.title('Training and validation accuracy')

plt.xlabel('Epochs')

plt.ylabel('Loss')

plt.legend()

plt.show()