train.py

import os
from operator import itemgetter    
import numpy as np
#import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
#get_ipython().magic(u'matplotlib inline')
#plt.style.use('ggplot')

import tensorflow as tf

from keras import models, regularizers, layers, optimizers, losses, metrics
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils, to_categorical
 
from keras.datasets import imdb

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

print("train_data ", train_data.shape)
print("train_labels ", train_labels.shape)
print("_"*100)
print("test_data ", test_data.shape)
print("test_labels ", test_labels.shape)
print("_"*100)

# See an actual review in words
# Reverse from integers to words using the DICTIONARY (given by keras...need to do nothing to create it)

word_index = imdb.get_word_index()

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

# VECTORIZE as one cannot feed integers into a NN
# Encoding the integer sequences into a binary matrix - one hot encoder basically
# From integers representing words, at various lengths - to a normalized one hot encoded tensor (matrix) of 10k columns

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)

print("x_train ", x_train.shape)
print("x_test ", x_test.shape)


# VECTORIZE the labels too - NO INTEGERS only floats into a tensor...(rare exceptions)

y_train = np.asarray(train_labels).astype('float32')
y_test = np.asarray(test_labels).astype('float32')
print("y_train ", y_train.shape)
print("y_test ", y_test.shape)

# Set a VALIDATION set

x_val = x_train[:10000]
partial_x_train = x_train[10000:]
y_val = y_train[:10000]
partial_y_train = y_train[10000:]

print("x_val ", x_val.shape)
print("partial_x_train ", partial_x_train.shape)
print("y_val ", y_val.shape)
print("partial_y_train ", partial_y_train.shape)

# NN MODEL

# Use of DROPOUT
#model = models.Sequential()
#model.add(layers.Dense(16, kernel_regularizer=regularizers.l1(0.001), activation='relu', input_shape=(10000,)))
#model.add(layers.Dropout(0.5))
#model.add(layers.Dense(16, kernel_regularizer=regularizers.l1(0.001),activation='relu'))
#model.add(layers.Dropout(0.5))
#model.add(layers.Dense(1, activation='sigmoid'))

# Use of REGULARIZATION
model = models.Sequential()
model.add(layers.Dense(16, kernel_regularizer=regularizers.l1_l2(l1=0.001, l2=0.001),activation='relu', input_shape=(10000,)))
model.add(layers.Dense(16, kernel_regularizer=regularizers.l1_l2(l1=0.001, l2=0.001),activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

# REGULARIZERS L1 L2
#regularizers.l1(0.001)
#regularizers.l2(0.001)
regularizers.l1_l2(l1=0.001, l2=0.001)

# OPTIMIZERS
#model.compile(optimizer=optimizers.RMSprop(lr=0.001), loss=losses.binary_crossentropy, metrics=[metrics.binary_accuracy])
model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])

# FIT / TRAIN model

NumEpochs = 28
BatchSize = 512

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

history = model.fit(partial_x_train, partial_y_train, epochs=NumEpochs, batch_size=BatchSize, validation_data=(x_val, y_val))

results = model.evaluate(x_test, y_test)
print("_"*100)
print("Test Loss and Accuracy")
print("results ", results)

model.save('./sentiment2.model.h5')