# Imports
import numpy as np
import keras
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(42)
This dataset comes preloaded with Keras, so one simple command will get us training and testing data. There is a parameter for how many words we want to look at. We've set it at 1000, but feel free to experiment.
# Loading the data (it's preloaded in Keras)
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=1000)
print(x_train.shape)
print(x_test.shape)
Notice that the data has been already pre-processed, where all the words have numbers, and the reviews come in as a vector with the words that the review contains. For example, if the word 'the' is the first one in our dictionary, and a review contains the word 'the', then there is a 1 in the corresponding vector.
The output comes as a vector of 1's and 0's, where 1 is a positive sentiment for the review, and 0 is negative.
print(x_train[0])
print(y_train[0])
Here, we'll turn the input vectors into (0,1)-vectors. For example, if the pre-processed vector contains the number 14, then in the processed vector, the 14th entry will be 1.
# One-hot encoding the output into vector mode, each of length 1000
tokenizer = Tokenizer(num_words=1000)
x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')
x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')
print(x_train[0])
And we'll also one-hot encode the output.
# One-hot encoding the output
num_classes = 2
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
print(y_train.shape)
print(y_test.shape)
Build a model here using sequential. Feel free to experiment with different layers and sizes! Also, experiment adding dropout to reduce overfitting.
# TODO: Build the model architecture
model = Sequential()
model.add(Dense(512, activation='relu', input_dim=1000))
model.add(Dropout(.5))
# model.add(Dense(256, activation='relu'))
# model.add(Dropout(.25))
model.add(Dense(2, activation='softmax'))
# TODO: Compile the model using a loss function and an optimizer.
model.compile(loss = 'categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
model.summary()
Run the model here. Experiment with different batch_size, and number of epochs!
# TODO: Run the model. Feel free to experiment with different batch sizes and number of epochs.
model.fit(x_train, y_train, epochs=10, batch_size=32, verbose=2)
This will give you the accuracy of the model, as evaluated on the testing set. Can you get something over 85%?
score = model.evaluate(x_test, y_test)
print("Accuracy: ", score[1])