(batch,
np.zeros((batch_size - len(labels), input_num, dims_num))))
labels_pre.extend(model.predict_on_batch(batch)[0:len(labels)])
labels_true.extend(labels)
steps += 1
print("%d/%d batch" % (steps, batch_num))
labels_pre = np.array(labels_pre).round()
def to_y(labels):
y = []
for i in range(len(labels)):
if labels[i][0] == 1:
y.append(0)
else:
y.append(1)
return y
y_true = to_y(labels_true)
y_pre = to_y(labels_pre)
precision = precision_score(y_true, y_pre)
recall = recall_score(y_true, y_pre)
print("Precision score is :", precision)
print("Recall score is :", recall)
if __name__ == "__main__":
train_generator, test_generator, train_size, test_size, input_num, dims_num = build_dataset(
batch_size)
train(train_generator, train_size, input_num, dims_num)
test(model_dir, test_generator, test_size, input_num, dims_num)
init_session()
batch_size=350
epochs_num=1
process_datas_dir="file\\process_datas.pickle"
log_dir="log\\LSTM.log"
model_dir="file\\LSTM_model"
def train(train_generator,train_size,input_num,dims_num):
print("Start Train Job! ")
start=time.time()
inputs=InputLayer(input_shape=(input_num,dims_num),batch_size=batch_size)
layer1=LSTM(128)
output=Dense(2,activation="softmax",name="Output")
optimizer=Adam()
model=Sequential()
model.add(inputs)
model.add(layer1)
model.add(Dropout(0.5))
model.add(output)
call=TensorBoard(log_dir=log_dir,write_grads=True,histogram_freq=1)
model.compile(optimizer,loss="categorical_crossentropy",metrics=["accuracy"])
model.fit_generator(train_generator,steps_per_epoch=train_size//batch_size,epochs=epochs_num,callbacks=[call])
# model.fit_generator(train_generator, steps_per_epoch=5, epochs=5, callbacks=[call])
model.save(model_dir)
end=time.time()
print("Over train job in %f s"%(end-start))
if __name__=="__main__":
train_generator, test_generator, train_size, test_size, input_num, dims_num=build_dataset(batch_size)
train(train_generator,train_size,input_num,dims_num)
test(model_dir,test_generator,test_size,input_num,dims_num,batch_size)