Блог

Я пытался понять, откуда у меня эта ошибка, и просмотрел формы здесь, в переполнении стека. Кажется, я не могу это исправить. Tensorflow 2.3 Я пытаюсь построить модель LSTM с несколькими входами

Входные данные:

 print(plot_data)
tensor_stack = tf.stack([plot_data,plot_data2])
print(tensor_stack)

tf.Tensor(
[[39.0991  39.09879 39.09845 ...  0.       0.       0.     ]
 [39.09889 39.09914 39.09887 ...  0.       0.       0.     ]
 [39.09889 39.09847 39.09858 ...  0.       0.       0.     ]
 [39.09889 39.09886 39.0989  ... 39.09906 39.09923 39.09919]], shape=(4, 484), dtype=float64)
tf.Tensor(
[[[ 39.0991   39.09879  39.09845 ...   0.        0.        0.     ]
  [ 39.09889  39.09914  39.09887 ...   0.        0.        0.     ]
  [ 39.09889  39.09847  39.09858 ...   0.        0.        0.     ]
  [ 39.09889  39.09886  39.0989  ...  39.09906  39.09923  39.09919]]

 [[-77.57008 -77.5702  -77.57081 ...   0.        0.        0.     ]
  [-77.57014 -77.56999 -77.57015 ...   0.        0.        0.     ]
  [-77.57012 -77.5699  -77.57022 ...   0.        0.        0.     ]
  [-77.57033 -77.56998 -77.56963 ... -77.5701  -77.56991 -77.56992]]], shape=(2, 4, 484), dtype=float64)
 

Это моя модель:

 import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam, RMSprop
import numpy as np

#from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from tensorflow import keras

from tensorflow.keras import layers

input1 = Input(shape=(plot_data.shape[0],plot_data.shape[1],))
input2 = Input(shape=(plot_data2.shape[0],plot_data.shape[1], ))
input = Concatenate()([input1, input2])
x = Dense(2)(input)
x = Dense(1)(x)
model = Model(inputs=[input1, input2], outputs=x)
model.summary()

print(input.shape[1])
print(input.shape[2])
maxlen = 484
#x_train = keras.preprocessing.sequence.pad_sequences(plot_data)
#x_train = x_train.reshape(-1, input.shape[1], input.shape[2])

# design network
model = Sequential()
model.add(LSTM(2, input_shape=( tensor_stack.shape[1],tensor_stack.shape[2]), return_sequences=True))
model.add(Dense(1))
model.compile(optimizer='adam',loss='mae', metrics=['mae'])
# fit network
history = model.fit(tensor_stack, epochs=50, batch_size=72, verbose=1, shuffle=False)