Keras CNN错误：预期序列有3维，但得到了形状为（500400）的数组

Question

我得到这个错误：

ValueError：检查输入时出错：预期序列有3个维度，但得到了形状为（500400）的数组

以下是我正在使用的代码。

print(X1_Train.shape)
print(X2_Train.shape)
print(y_train.shape)

输出（这里每行有500行）：

(500, 400)
(500, 1500)
(500,)

400 => timesteps (below)
1500 => n (below)

代码：

timesteps = 50 * 8
n = 50 * 30

def createClassifier():
    sequence = Input(shape=(timesteps, 1), name='Sequence')
    features = Input(shape=(n,), name='Features')

    conv = Sequential()
    conv.add(Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1)))
    conv.add(Conv1D(10, 5, activation='relu'))
    conv.add(MaxPool1D(2))
    conv.add(Dropout(0.5))

    conv.add(Conv1D(5, 6, activation='relu'))
    conv.add(Conv1D(5, 6, activation='relu'))
    conv.add(MaxPool1D(2))
    conv.add(Dropout(0.5))
    conv.add(Flatten())
    part1 = conv(sequence)

    merged = concatenate([part1, features])

    final = Dense(512, activation='relu')(merged)
    final = Dropout(0.5)(final)
    final = Dense(num_class, activation='softmax')(final)

    model = Model(inputs=[sequence, features], outputs=[final])
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

model = createClassifier()
# print(model.summary())
history = model.fit([X1_Train, X2_Train], y_train, epochs =5)

有什么见解吗？

Answer 1

两件事-

Conv1D层希望输入的形状为（batch\u size，x，filters），在您的情况下为（500400,1）
您需要重塑输入层的形状，添加另一个大小为1的轴。（这不会更改数据中的任何内容）

您试图使用多个输入，顺序API并不是最好的选择。我建议使用函数式API

编辑：关于您的评论，不确定您做错了什么，但这是您的代码的工作版本（包含虚假数据），带有重塑：

import keras

import numpy as np



X1_Train = np.ones((500,400))
X2_Train = np.ones((500,1500))
y_train = np.ones((500))
print(X1_Train.shape)
print(X2_Train.shape)
print(y_train.shape)

num_class = 1


timesteps = 50 * 8
n = 50 * 30

def createClassifier():
    sequence = keras.layers.Input(shape=(timesteps, 1), name='Sequence')
    features = keras.layers.Input(shape=(n,), name='Features')

    conv = keras.Sequential()
    conv.add(keras.layers.Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1)))
    conv.add(keras.layers.Conv1D(10, 5, activation='relu'))
    conv.add(keras.layers.MaxPool1D(2))
    conv.add(keras.layers.Dropout(0.5))

    conv.add(keras.layers.Conv1D(5, 6, activation='relu'))
    conv.add(keras.layers.Conv1D(5, 6, activation='relu'))
    conv.add(keras.layers.MaxPool1D(2))
    conv.add(keras.layers.Dropout(0.5))
    conv.add(keras.layers.Flatten())
    part1 = conv(sequence)

    merged = keras.layers.concatenate([part1, features])

    final = keras.layers.Dense(512, activation='relu')(merged)
    final = keras.layers.Dropout(0.5)(final)
    final = keras.layers.Dense(num_class, activation='softmax')(final)

    model = keras.Model(inputs=[sequence, features], outputs=[final])
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

model = createClassifier()
# print(model.summary())
X1_Train = X1_Train.reshape((500,400,1))
history = model.fit([X1_Train, X2_Train], y_train, epochs =5)

带输出：

Using TensorFlow backend.
(500, 400)
(500, 1500)
(500,)
Epoch 1/5
500/500 [==============================] - 1s 3ms/step - loss: 1.1921e-07 - acc: 1.0000
Epoch 2/5
500/500 [==============================] - 0s 160us/step - loss: 1.1921e-07 - acc: 1.0000
Epoch 3/5
500/500 [==============================] - 0s 166us/step - loss: 1.1921e-07 - acc: 1.0000
Epoch 4/5
500/500 [==============================] - 0s 154us/step - loss: 1.1921e-07 - acc: 1.0000
Epoch 5/5
500/500 [==============================] - 0s 157us/step - loss: 1.1921e-07 - acc: 1.0000