model.add(Input(shape=(X_train.shape[1], X_train.shape[2]*5)))修改该代码,解决 ValueError: Input 0 of layer "sequential_76" is incompatible with the layer: expected shape=(None, 10, 5), found shape=(None, 10, 1, 1, 5)问题
时间: 2024-01-30 11:03:49 浏览: 26
根据报错信息,我们可以看到原本预期的输入形状是 `(None, 10, 5)`,但实际输入的形状是 `(None, 10, 1, 1, 5)`,多了两个维度。因此,我们需要将多余的维度去掉,可以使用 `np.squeeze()` 函数来实现。
修改后的代码如下:
```python
model.add(Input(shape=np.squeeze((X_train.shape[1], X_train.shape[2]*5)))))
```
这样就可以将输入形状变为预期的 `(None, 10, 5)`。
相关问题
from keras.datasets import mnist vae = Model(input_img, y) vae.compile(optimizer='rmsprop', loss=None) vae.summary() (x_train, _), (x_test, y_test) = mnist.load_data() x_train = x_train.astype('float32') / 255. x_train = x_train.reshape(x_train.shape + (1,)) x_test = x_test.astype('float32') / 255. x_test = x_test.reshape(x_test.shape + (1,)) vae.fit(x=x_train, y=None, shuffle=True, epochs=10, batch_size=batch_size, validation_data=(x_test, None))
这段代码缺少定义`input_img`和`y`的语句。在VAE模型中,通常使用编码器和解码器两个部分来实现。编码器将输入图片压缩到一个低维潜在空间中,解码器则将潜在空间向量转换回图像。
因此,需要定义输入层`input_img`和输出层`y`。可以按照以下方式定义:
```python
input_img = Input(shape=(28, 28, 1))
# 编码器
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(latent_dim, (3, 3), activation='relu', padding='same')(x)
# 潜在空间采样
z_mean = Flatten()(x)
z_log_var = Flatten()(x)
z = Lambda(sampling)([z_mean, z_log_var])
# 解码器
decoder_input = Input(K.int_shape(z)[1:])
x = Reshape((7, 7, 16))(decoder_input)
x = Conv2DTranspose(128, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2DTranspose(64, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2DTranspose(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2DTranspose(1, (3, 3), activation='sigmoid', padding='same')(x)
decoder = Model(decoder_input, x)
# 完整的 VAE 模型
outputs = decoder(z)
vae = Model(input_img, outputs)
# 定义损失函数
reconstruction_loss = binary_crossentropy(K.flatten(input_img), K.flatten(outputs))
reconstruction_loss *= img_rows * img_cols
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
# 编译模型
vae.compile(optimizer='rmsprop')
```
这里的`latent_dim`是潜在空间的维度,`sampling`是一个自定义的采样函数,用来从潜在空间中采样。同时,定义了一个解码器`decoder`,用于将潜在空间向量转换为图像。最后,使用`vae.add_loss()`来定义整个VAE模型的损失函数。
希望这可以帮助你解决问题!
def create_LSTM_model(): # instantiate the model model = Sequential() X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features))) model.add(Flatten()) model.add(RepeatVector(n_outputs)) model.add(MaxPooling1D()) # 添加lstm层 model.add(LSTM(64, activation = 'relu', return_sequences=True)) model.add(Dropout(0.5)) #添加注意力层 model.add(LSTM(64, activation = 'relu', return_sequences=False)) # 添加dropout model.add(Dropout(0.5)) model.add(Dense(128)) # 输出层 model.add(Dense(1, name='Output')) # 编译模型 model.compile(optimizer='adam', loss='mse', metrics=['mae']) return model # lstm network model = create_LSTM_model() # summary print(model.summary())修改该代码,解决UnboundLocalError Traceback (most recent call last) <ipython-input-21-b28a080066f3> in <module> 46 return model 47 # lstm network ---> 48 model = create_LSTM_model() 49 # summary 50 print(model.summary()) <ipython-input-21-b28a080066f3> in create_LSTM_model() 12 # instantiate the model 13 model = Sequential() ---> 14 X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) 15 model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) 16 UnboundLocalError: local variable 'X_train' referenced before assignment问题
这个错误是因为 `X_train` 变量在函数内部被重新定义,但在 `X_train.reshape()` 被调用时,该变量尚未被赋值。解决方法是将 `X_train` 作为参数传递给函数,并在调用函数时将其传递。修改后的代码如下:
```
def create_LSTM_model(X_train):
# instantiate the model
model = Sequential()
X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features))
model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))
# cnn1d Layers
model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features)))
model.add(Flatten())
model.add(RepeatVector(n_outputs))
model.add(MaxPooling1D())
# 添加lstm层
model.add(LSTM(64, activation='relu', return_sequences=True))
model.add(Dropout(0.5))
#添加注意力层
model.add(LSTM(64, activation='relu', return_sequences=False))
# 添加dropout
model.add(Dropout(0.5))
model.add(Dense(128))
# 输出层
model.add(Dense(1, name='Output'))
# 编译模型
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
return model
# lstm network
X_train = ...
model = create_LSTM_model(X_train)
# summary
print(model.summary())
```