def test_model():
features, labels = loadFromPickle()
features, labels = shuffle(features, labels)
features = features.reshape(features.shape[0], 64, 20, 1)
labels = prepress_labels(labels)
train_x, test_x, train_y, test_y = train_test_split(features,
labels,
random_state=0,
test_size=0.1)
model, callbacks_list = keras_model((
64,
20,
1,
), len(labels[0]))
print_summary(model)
model.fit(train_x,
train_y,
batch_size=128,
epochs=5,
verbose=1,
validation_data=(test_x, test_y),
callbacks=[TensorBoard(log_dir="TensorBoard")])
# 开始评估模型效果 # verbose=0为不输出日志信息
score = model.evaluate(test_x, test_y, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1]) # 准确度
model.save('asr_mfcc_conv2d_model.h5') # 保存训练模型
def test_model1():
features, labels = loadFromPickle()
labels_count = int(len(labels) / MAX_NUM)
# features, labels = augmentData(features, labels)
features, labels = shuffle(features, labels)
labels = prepress_labels(labels)
train_x, test_x, train_y, test_y = train_test_split(features,
labels,
random_state=0,
test_size=0.1)
# train_x = train_x.reshape(train_x.shape[0], 28, 28, 1)
# test_x = test_x.reshape(test_x.shape[0], 28, 28, 1)
model, callbacks_list = keras_model1((28 * 28, ), labels_count)
print_summary(model)
model.fit(train_x,
train_y,
validation_data=(test_x, test_y),
epochs=500,
batch_size=256)
# model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=500, batch_size=256,
# callbacks=[TensorBoard(log_dir="TensorBoard")])
# 开始评估模型效果 # verbose=0为不输出日志信息
score = model.evaluate(test_x, test_y, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1]) # 准确度
model.save('model1.h5')
def test_model():
features, labels = loadFromPickle()
labels_count = len(load_label_name())
# features, labels = augmentData(features, labels)
features, labels = shuffle(features, labels)
labels = prepress_labels(labels)
train_x, test_x, train_y, test_y = train_test_split(features,
labels,
random_state=0,
test_size=0.2)
train_x = train_x.reshape(train_x.shape[0], 28, 28, 1)
test_x = test_x.reshape(test_x.shape[0], 28, 28, 1)
model, callbacks_list = keras_model((
28,
28,
1,
), labels_count)
print_summary(model)
model.fit(train_x,
train_y,
validation_data=(test_x, test_y),
epochs=40,
batch_size=128)
# 开始评估模型效果 # verbose=0为不输出日志信息
score = model.evaluate(test_x, test_y, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1]) # 准确度
model.save('model.h5')
def test_model2(input_length):
features, labels = loadFromPickle()
features, labels = shuffle(features, labels)
features=features.reshape(features.shape[0],input_length)
labels=prepress_labels(labels)
train_x, test_x, train_y, test_y = train_test_split(features, labels, random_state=0,
test_size=0.2)
model, callbacks_list = keras_model2((input_length,),len(labels[0]))
print_summary(model)
model.fit(train_x, train_y, batch_size=128, epochs=20, verbose=1, validation_data=(test_x, test_y),
callbacks=[TensorBoard(log_dir="TensorBoard")])
score = model.evaluate(test_x, test_y, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1]) # 准确度
model.save('test_model2.h5') # 保存训练模型
def validate():
data_dir = ".\\data\\"
label_names = get_labels(data_dir)
model = load_model('asr_model.h5') # 加载训练模型
features, labels = loadFromPickle()
features, labels = shuffle(features, labels)
features=features.reshape(features.shape[0],20,32,1)
labels=prepress_labels(labels)
train_x, test_x, train_y, test_y = train_test_split(features, labels, random_state=0,
test_size=0.3)
print_summary(model)
# 开始评估模型效果 # verbose=0为不输出日志信息
score = model.evaluate(test_x, test_y, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1]) # 准确度