Keras - IMDB Sentiment-Analysis Keras and TensorFlow

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep  6 08:49:48 2019

@author: onno
"""

# IMPORT MODULES
# TURN ON the GPU !!!
# If importing dataset from outside - like this IMDB - Internet must be "connected"

import os
from operator import itemgetter    
import numpy as np
import pandas as pd
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

print(os.getcwd())
print("Modules imported \n")
print("Files in current directory:")
from subprocess import check_output
print(check_output(["ls", "./input"]).decode("utf8")) #check the files available in the directory

# LOAD IMDB DATA
# (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
import numpy as np
# save np.load
np_load_old = np.load
# modify the default parameters of np.load
np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)
# call load_data with allow_pickle implicitly set to true
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
# restore np.load for future normal usage
np.load = np_load_old
 

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)
print("Maximum value of a word index ")
print(max([max(sequence) for sequence in train_data]))
print("Maximum length num words of review in train ")
print(max([len(sequence) for sequence in train_data]))

# 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()])

decoded_review = ' '.join(
[reverse_word_index.get(i - 3, '?') for i in train_data[123]])

print(decoded_review)
 

# 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 = 10
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)
history_dict = history.history
history_dict.keys()

# VALIDATION LOSS curves

plt.clf()
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, (len(history_dict['loss']) + 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()

# VALIDATION ACCURACY curves

plt.clf()
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
epochs = range(1, (len(history_dict['acc']) + 1))
plt.plot(epochs, acc_values, 'bo', label='Training acc')
plt.plot(epochs, val_acc_values, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

# PREDICT

model.predict(x_test)

Pranala Menarik

• Keras