load_and_test.py

from __future__ import print_function

import os
import sys

import numpy as np
import keras

from sentence_types import load_encoded_data
from sentence_types import encode_data
from sentence_types import get_custom_test_comments

from keras.preprocessing import sequence
from keras.models import Sequential, model_from_json
from keras.layers import Dense, Dropout, Activation, Embedding
from keras.layers import Conv1D, GlobalMaxPooling1D

from keras.preprocessing.text import Tokenizer

# Where to store model
model_name      = "models/cnn"

# Where to get the training/test data
embedding_name  = "data/default"

# Model configuration
max_words = 110000
maxlen = 500
batch_size = 64
embedding_dims = 75
filters = 100
kernel_size = 5
hidden_dims = 350
epochs = 7

# Add parts-of-speech to data
pos_tags_flag = True


# Export & load embeddings
x_train, x_test, y_train, y_test = load_encoded_data(data_split=0.8, embedding_name=embedding_name, pos_tags=pos_tags_flag)
num_classes = np.max(y_train) + 1
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# load model
json_file = open(model_name + '.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)

# load weights into new model
model.load_weights(model_name + ".h5")

# evaluate loaded model on test data
model.compile(loss='categorical_crossentropy',  optimizer='adam', metrics=['accuracy'])
score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
print('Test accuracy:', score[1])

# Run the model on some random test data..
test_comments, test_comments_category = get_custom_test_comments()
x_test, _, y_test, _ = encode_data(test_comments, test_comments_category, data_split=1.0,
                                   embedding_name=embedding_name, add_pos_tags_flag=pos_tags_flag)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
y_test = keras.utils.to_categorical(y_test, num_classes)

score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
print('Manual test')
print('Test accuracy:', score[1])

# Show predictions against our random data.
print(len(x_test))
predictions = model.predict(x_test, batch_size=batch_size, verbose=1)
real = []
test = []
for i in range(0, len(predictions)):
    real.append(y_test[i].argmax(axis=0))
    test.append(predictions[i].argmax(axis=0))

print("Predictions")
print("Real", real)
print("Test", test)