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'''
Written by Austin Walters
Last Edit: January 2, 2019
For use on austingwalters.com
A CNN to classify a sentence as one
of the common sentance types:
Question, Statement, Command, Exclamation
Heavily Inspired by Keras Examples:
https://github.com/keras-team/keras
'''
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
# Use can load a different model if desired
model_name = "models/cnn"
embedding_name = "data/default"
load_model_flag = False
arguments = sys.argv[1:len(sys.argv)]
if len(arguments) == 1:
model_name = arguments[0]
load_model_flag = os.path.isfile(model_name+".json")
print(model_name)
print("Load Model?", (not load_model_flag))
# Model configuration
max_words = 15000
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
print(num_classes, 'classes')
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('Convert class vector to binary class matrix '
'(for use with categorical_crossentropy)')
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
if not load_model_flag:
print('Constructing model!')
model = Sequential()
model.add(Embedding(max_words, embedding_dims,
input_length=maxlen))
model.add(Dropout(0.2))
model.add(Conv1D(filters, kernel_size, padding='valid',
activation='relu', strides=1))
model.add(GlobalMaxPooling1D())
model.add(Dense(hidden_dims))
model.add(Dropout(0.2))
model.add(Activation('relu'))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test))
model_json = model.to_json()
with open(model_name + ".json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights(model_name + ".h5")
print("Saved model to disk")
else:
print('Loading model!')
# load json and create 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")
print("Loaded model from disk")
# 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])
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
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)