Tôi nghĩ rằng bạn có thể có thể sử dụng công việc trước đây của tôi. Trong mã này, tôi tạo ra các sóng hình sin (có bước sóng và pha ngẫu nhiên) và huấn luyện một LSTM đến một chuỗi các điểm từ các sóng hình sin này và tạo ra một chuỗi 150 điểm hoàn thành mỗi sóng hình sin.
Đây là mô hình:
features_num=5
latent_dim=40
##
encoder_inputs = Input(shape=(None, features_num))
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoder_inputs)
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoded)
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoded)
encoded = LSTM(latent_dim, return_state=True)(encoded)
encoder = Model (input=encoder_inputs, output=encoded)
##
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]
decoder_inputs=Input(shape=(1, features_num))
decoder_lstm_1 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_2 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_3 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_4 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_dense = Dense(features_num)
all_outputs = []
inputs = decoder_inputs
states_1=encoder_states
# Place holder values:
states_2=states_1; states_3=states_1; states_4=states_1
for _ in range(1):
# Run the decoder on the first timestep
outputs_1, state_h_1, state_c_1 = decoder_lstm_1(inputs, initial_state=states_1)
outputs_2, state_h_2, state_c_2 = decoder_lstm_2(outputs_1)
outputs_3, state_h_3, state_c_3 = decoder_lstm_3(outputs_2)
outputs_4, state_h_4, state_c_4 = decoder_lstm_4(outputs_3)
# Store the current prediction (we will concatenate all predictions later)
outputs = decoder_dense(outputs_4)
all_outputs.append(outputs)
# Reinject the outputs as inputs for the next loop iteration
# as well as update the states
inputs = outputs
states_1 = [state_h_1, state_c_1]
states_2 = [state_h_2, state_c_2]
states_3 = [state_h_3, state_c_3]
states_4 = [state_h_4, state_c_4]
for _ in range(149):
# Run the decoder on each timestep
outputs_1, state_h_1, state_c_1 = decoder_lstm_1(inputs, initial_state=states_1)
outputs_2, state_h_2, state_c_2 = decoder_lstm_2(outputs_1, initial_state=states_2)
outputs_3, state_h_3, state_c_3 = decoder_lstm_3(outputs_2, initial_state=states_3)
outputs_4, state_h_4, state_c_4 = decoder_lstm_4(outputs_3, initial_state=states_4)
# Store the current prediction (we will concatenate all predictions later)
outputs = decoder_dense(outputs_4)
all_outputs.append(outputs)
# Reinject the outputs as inputs for the next loop iteration
# as well as update the states
inputs = outputs
states_1 = [state_h_1, state_c_1]
states_2 = [state_h_2, state_c_2]
states_3 = [state_h_3, state_c_3]
states_4 = [state_h_4, state_c_4]
# Concatenate all predictions
decoder_outputs = Lambda(lambda x: K.concatenate(x, axis=1))(all_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
#model = load_model('pre_model.h5')
print(model.summary())
Và đây là toàn bộ kịch bản:
from keras.models import Model
from keras.layers import Input, LSTM, Dense, TimeDistributed,Lambda, Dropout, Activation ,RepeatVector
from keras.callbacks import ModelCheckpoint
import numpy as np
from keras.layers import Lambda
from keras import backend as K
from keras.models import load_model
import os
features_num=5
latent_dim=40
##
encoder_inputs = Input(shape=(None, features_num))
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoder_inputs)
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoded)
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoded)
encoded = LSTM(latent_dim, return_state=True)(encoded)
encoder = Model (input=encoder_inputs, output=encoded)
##
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]
decoder_inputs=Input(shape=(1, features_num))
decoder_lstm_1 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_2 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_3 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_4 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_dense = Dense(features_num)
all_outputs = []
inputs = decoder_inputs
# Place holder values:
states_1=encoder_states
states_2=states_1; states_3=states_1; states_4=states_1
for _ in range(1):
# Run the decoder on one timestep
outputs_1, state_h_1, state_c_1 = decoder_lstm_1(inputs, initial_state=states_1)
outputs_2, state_h_2, state_c_2 = decoder_lstm_2(outputs_1)
outputs_3, state_h_3, state_c_3 = decoder_lstm_3(outputs_2)
outputs_4, state_h_4, state_c_4 = decoder_lstm_4(outputs_3)
# Store the current prediction (we will concatenate all predictions later)
outputs = decoder_dense(outputs_4)
all_outputs.append(outputs)
# Reinject the outputs as inputs for the next loop iteration
# as well as update the states
inputs = outputs
states_1 = [state_h_1, state_c_1]
states_2 = [state_h_2, state_c_2]
states_3 = [state_h_3, state_c_3]
states_4 = [state_h_4, state_c_4]
for _ in range(149):
# Run the decoder on one timestep
outputs_1, state_h_1, state_c_1 = decoder_lstm_1(inputs, initial_state=states_1)
outputs_2, state_h_2, state_c_2 = decoder_lstm_2(outputs_1, initial_state=states_2)
outputs_3, state_h_3, state_c_3 = decoder_lstm_3(outputs_2, initial_state=states_3)
outputs_4, state_h_4, state_c_4 = decoder_lstm_4(outputs_3, initial_state=states_4)
# Store the current prediction (we will concatenate all predictions later)
outputs = decoder_dense(outputs_4)
all_outputs.append(outputs)
# Reinject the outputs as inputs for the next loop iteration
# as well as update the states
inputs = outputs
states_1 = [state_h_1, state_c_1]
states_2 = [state_h_2, state_c_2]
states_3 = [state_h_3, state_c_3]
states_4 = [state_h_4, state_c_4]
# Concatenate all predictions
decoder_outputs = Lambda(lambda x: K.concatenate(x, axis=1))(all_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
#model = load_model('pre_model.h5')
print(model.summary())
model.compile(loss='mean_squared_error', optimizer='adam')
def create_wavelength(min_wavelength, max_wavelength, fluxes_in_wavelength, category ) :
#category :: 0 - train ; 2 - validate ; 4- test. 1;3;5 - dead space
c=(category+np.random.random())/6
k = fluxes_in_wavelength
#
base= (np.trunc(k*np.random.random()*(max_wavelength-min_wavelength)) +k*min_wavelength) /k
answer=base+c/k
return (answer)
def make_line(length,category):
shift= np.random.random()
wavelength = create_wavelength(30,10,1,category)
a=np.arange(length)
answer=np.sin(a/wavelength+shift)
return answer
def make_data(seq_num,seq_len,dim,category):
data=np.array([]).reshape(0,seq_len,dim)
for i in range (seq_num):
mini_data=np.array([]).reshape(0,seq_len)
for j in range (dim):
line = make_line(seq_len,category)
line=line.reshape(1,seq_len)
mini_data=np.append(mini_data,line,axis=0)
mini_data=np.swapaxes(mini_data,1,0)
mini_data=mini_data.reshape(1,seq_len,dim)
data=np.append(data,mini_data,axis=0)
return (data)
def train_generator():
while True:
sequence_length = np.random.randint(150, 300)+150
data=make_data(1000,sequence_length,features_num,0) # category=0 in train
# decoder_target_data is the same as decoder_input_data but offset by one timestep
encoder_input_data = data[:,:-150,:] # all but last 150
decoder_input_data = data[:,-151,:] # the one before the last 150.
decoder_input_data=decoder_input_data.reshape((decoder_input_data.shape[0],1,decoder_input_data.shape[1]))
decoder_target_data = (data[:, -150:, :]) # last 150
yield [encoder_input_data, decoder_input_data], decoder_target_data
def val_generator():
while True:
sequence_length = np.random.randint(150, 300)+150
data=make_data(1000,sequence_length,features_num,2) # category=2 in val
encoder_input_data = data[:,:-150,:] # all but last 150
decoder_input_data = data[:,-151,:] # the one before the last 150.
decoder_input_data=decoder_input_data.reshape((decoder_input_data.shape[0],1,decoder_input_data.shape[1]))
decoder_target_data = (data[:, -150:, :]) # last 150
yield [encoder_input_data, decoder_input_data], decoder_target_data
filepath_for_w= 'flux_p2p_s2s_model.h5'
checkpointer=ModelCheckpoint(filepath_for_w, monitor='val_loss', verbose=0, save_best_only=True, mode='auto', period=1)
model.fit_generator(train_generator(),callbacks=[checkpointer], steps_per_epoch=30, epochs=2000, verbose=1,validation_data=val_generator(),validation_steps=30)
model.save(filepath_for_w)
def predict_wave(input_wave,input_for_decoder): # input wave= x[n,:,:], ie points except the last 150; each wave has feature_num features. run this function for all such instances (=n)
#print (input_wave.shape)
#print (input_for_decoder.shape)
pred= model.predict([input_wave,input_for_decoder])
return pred
def predict_many_waves_from_input(x):
x, x2=x # x == encoder_input_data ; x==2 decoder_input_data
instance_num= x.shape[0]
multi_predict_collection=np.zeros((x.shape[0],150,x.shape[2]))
for n in range(instance_num):
input_wave=x[n,:,:].reshape(1,x.shape[1],x.shape[2])
input_for_decoder=x2[n,:,:].reshape(1,x2.shape[1],x2.shape[2])
wave_prediction=predict_wave(input_wave,input_for_decoder)
multi_predict_collection[n,:,:]=wave_prediction
return (multi_predict_collection)
def test_maker():
if True:
sequence_length = np.random.randint(150, 300)+150
data=make_data(470,sequence_length,features_num,4) # category=4 in test
encoder_input_data = data[:,:-150,:] # all but last 150
decoder_input_data = data[:,-151,:] # the one before the last 150.
decoder_input_data=decoder_input_data.reshape((decoder_input_data.shape[0],1,decoder_input_data.shape[1]))
decoder_target_data = (data[:, -150:, :]) # last 150
return [encoder_input_data, decoder_input_data], decoder_target_data
x,y= test_maker()
a=predict_many_waves_from_input (x) # is that right..?
x=x[0] # keep the wave (generated data except last 150 time points)
print (x.shape)
print (y.shape)
print (a.shape)
np.save ('a.npy',a)
np.save ('y.npy',y)
np.save ('x.npy',x)
print (np.mean(np.absolute(y[:,:,0]-a[:,:,0])))
print (np.mean(np.absolute(y[:,:,1]-a[:,:,1])))
print (np.mean(np.absolute(y[:,:,2]-a[:,:,2])))
print (np.mean(np.absolute(y[:,:,3]-a[:,:,3])))
print (np.mean(np.absolute(y[:,:,4]-a[:,:,4])))