predict.py

#!/usr/bin/env python

from load_data_amz import SentimentsData
from keras.models import load_model
from keras.optimizers import Adam
from keras.preprocessing import sequence

print("Loading model...")
model = load_model("models/sentiments_full_glove_embeddings.hdf5")
model.compile(loss='binary_crossentropy',
              optimizer=Adam(),
              metrics=['accuracy'])

model.summary()

print("Get the data to predict on...")
sd = SentimentsData()
print('Loading data...')
(x_train, y_train), (x_test, y_test) = sd.load()

max_features = sd.corpus_size
maxlen = 1000 #sd.max_size  # cut texts after this number of words (among top max_features most common words)
batch_size = 64
sd.create_embeddings_matrix()

print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)

print('Predict...')
score, acc = model.evaluate(x_train, y_train,
                            batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)