input_image = Input(shape=(L, H)) dropout1 = Dropout(0.25)(input_image) dense1 = Dense(4096, input_shape=(L, H) , activation='relu')(dropout1) bn1 = BatchNormalization(momentum=0.93, axis=-1)(dense1) dropout2 = Dropout(0.25)(bn1) dense2 = Dense(4096, activation='relu')(dropout2) bn2 = BatchNormalization(momentum=0.93, axis=-1)(dense2) dropout3 = Dropout(0.25)(bn2) dense3 = Dense(35, activation='relu')(dropout3) input_numeric = Input(shape=(M,N)) concat = K.concatenate([input_numeric, dense3], axis=2) bn4 = BatchNormalization(momentum=0.93, axis=-1)(concat) dropout4 = Dropout(0.5)(bn4) output = Dense(26, activation='softmax')(dropout4) model = Model(inputs=[input_image, input_numeric], outputs=output) model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy']) model.fit([train_img_x, train_data_x], train_y, batch_size=15, epochs=10, validation_split=0.2) prediction = model.predict([train_img_x, train_data_x]) 报错`validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'torch.Tensor'>, <class 'torch.Tensor'>] ，如何解决

这个错误提示表明 `validation_split` 参数只支持 Tensor 或 NumPy 数组类型，但是这里输入的类型是 Torch Tensor。因此，需要将输入类型转换为 NumPy 数组类型。可以使用 `.numpy()` 方法将 Torch Tensor 转换为 NumPy 数组，例如：

prediction = model.predict([train_img_x.numpy(), train_data_x.numpy()])

这样就可以将 Torch Tensor 转换为 NumPy 数组，然后传递给 `predict` 方法进行预测。

相关问题

input_image = Input(shape=(30, 30)) dropout1 = Dropout(0.25)(input_image) dense1 = Dense(4096, input_shape=(30, 30))(dropout1) bn1 = BatchNormalization(momentum=0.93, axis=-1)(dense1) dropout2 = Dropout(0.25)(bn1) dense2 = Dense(4096, activation='relu')(dropout2) bn2 = BatchNormalization(momentum=0.93, axis=-1)(dense2) dropout3 = Dropout(0.25)(bn2) dense3 = Dense(35, activation='relu')(dropout3) input_numeric = Input(shape=(30,30)) concat = concatenate((input_numeric.numpy(), dense3.numpy()), axis=1) 中报错'Tensor' object has no attribute 'numpy' 如何解决

在代码中，`input_numeric`和`dense3`都是Tensor对象，而Tensor对象没有`numpy`属性。你需要使用`K.concatenate()`代替`concatenate()`，并将`input_numeric`和`dense3`作为参数传递给`K.concatenate()`。修改后的代码如下：

import keras.backend as K

input_image = Input(shape=(30, 30))
dropout1 = Dropout(0.25)(input_image)
dense1 = Dense(4096, input_shape=(30, 30))(dropout1)
bn1 = BatchNormalization(momentum=0.93, axis=-1)(dense1)
dropout2 = Dropout(0.25)(bn1)
dense2 = Dense(4096, activation='relu')(dropout2)
bn2 = BatchNormalization(momentum=0.93, axis=-1)(dense2)
dropout3 = Dropout(0.25)(bn2)
dense3 = Dense(35, activation='relu')(dropout3)
input_numeric = Input(shape=(30,30))
concat = K.concatenate([input_numeric, dense3], axis=2)

这样就不会出现上述报错了。

input_image = Input(shape=(L, H)) dropout1 = Dropout(0.25)(input_image) dense1 = Dense(4096, input_shape=(L, H) , activation='relu')(dropout1) dropout2 = Dropout(0.25)(dense1) dense2 = Dense(4096, activation='relu')(dropout2) dropout3 = Dropout(0.25)(dense2) dense3 = Dense(35, activation='relu')(dropout3) input_numeric = Input(shape=(M,N)) concat = K.concatenate([input_numeric, dense3], axis=2) dropout4 = Dropout(0.5)(concat) output = Dense(26, activation='softmax')(dropout4) model = Model(inputs=[input_image, input_numeric], outputs=output) model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy']) model.fit([train_img_x, train_data_x], train_y, batch_size=15, epochs=10, validation_split=0.2) 报错validation_split is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'torch.Tensor'>, <class 'torch.Tensor'>] ，如何解决

这个报错是因为你在使用 PyTorch 的张量作为输入数据，而 `validation_split` 参数只支持使用 Tensor 或 NumPy 数组作为输入数据。要解决这个问题，你可以将 PyTorch 张量转换为 NumPy 数组或 Tensor 对象，然后再将其传递给 `validation_split` 参数。你可以使用以下代码来转换 PyTorch 张量为 NumPy 数组：

data_numpy = data_tensor.cpu().numpy()

其中，`data_tensor` 是你要转换的 PyTorch 张量。

另外，你还需要将训练数据和验证数据都转换为 NumPy 数组或 Tensor 对象，然后再将其传递给 `model.fit()` 方法。你可以使用以下代码将 PyTorch 张量转换为 Tensor 对象：

data_tensor = data_tensor.to(device)

其中，`device` 是你使用的设备，如 CPU 或 GPU。