TypeError：无法将 <class 'list'> 类型的对象转换为张量。内容：[无，-1, 3]。考虑将元素转换为支持的类型

Question

Tensorflow 中出现数据类型不匹配的错误。

我尝试过：

prediction = tf.convert_to_tensor(prediction)
y = tf.convert_to_tensor(y)

在传递给损失函数之前

def train():
    print("Training")

    # tf Graph input
    x = tf.placeholder(dtype=tf.float32, shape=[None, config.input_window_size - 1, config.input_size], name="input_sequence")
    y = tf.placeholder(dtype=tf.float32, shape=[None, config.output_window_size, config.input_size], name="raw_labels")
    dec_in = tf.placeholder(dtype=tf.float32, shape=[None, config.output_window_size, config.input_size], name="decoder_input")

    labels = tf.transpose(y, [1, 0, 2])
    labels = tf.reshape(labels, [-1, config.input_size])
    labels = tf.split(labels, config.output_window_size, axis=0, name='labels')

    tf.set_random_seed(112858)


    # Define model
    prediction = models.seq2seq(x, dec_in, config, True)

    sess = tf.Session()

    loss = eval('loss_functions.lie_loss(prediction, labels, config)')

    # Add a summary for the loss
    train_loss = tf.summary.scalar('train loss', loss)
    valid_loss = tf.summary.scalar('valid loss', loss)

损失函数

def lie_loss(prediction, y, config):
    # Compute the joint discrepancy following forward kinematics of lie parameters

    prediction = tf.concat(prediction, axis=0)
    y = tf.concat(y, axis=0)

    joint_pred = forward_kinematics(prediction, config)
    joint_label = forward_kinematics(y, config)
    loss = tf.reduce_mean(tf.square(tf.subtract(joint_pred, joint_label)))

    return loss

我得到错误 TypeError：无法将类型对象转换为张量。内容：[无，-1, 3]。考虑将元素转换为支持的类型。

在将预测和 y 更改为张量时，我在 Forward_kinematics 中收到以下错误：

joint_pred = forward_kinematics(prediction, config)
Prediction/src/loss_functions.py", line 101, in forward_kinematics
    for i in range(omega[0].shape[0]):

TypeError: __index__ returned non-int (type NoneType)

forward_kinematics 的函数如下：

def forward_kinematics(lie_parameters, config):
    print(lie_parameters)
    nframes = lie_parameters.get_shape().as_list()[0]
    print("nframs")
    print(nframes)
    # nframes = lie_parameters.shape[0]
    lie_parameters = tf.reshape(lie_parameters, [nframes, -1, 3])

    R = []
    idx = config.idx
    chain_idx = config.chain_idx
    # config bone params are retrieved from read_data.py

    bone_params = config.bone_params
    for h in range(nframes):
        omega = []
        A = []
        chain = []
        for i in range(len(idx) - 1):
            chain.append(tf.concat([lie_parameters[h, idx[i]:idx[i + 1]], tf.zeros([1, 3])], axis=0))

        omega.append(tf.concat(chain, axis=0))

        ##### I have to check this omega
        print("Omega")
        print(type(omega))
        print(omega)
        print(omega[0])
        print(omega[0].shape)
        print(omega[0].shape[0])

        for i in range(omega[0].shape[0]):
            A.append([rotmat(omega[0][i])])
        R.append(tf.concat(A, axis=0))

    R = tf.stack(R)
    joints = []
    for h in range(nframes):
        jointlist = []
        for i in range(len(chain_idx)):
            for j in range(chain_idx[i].shape[0]):
                if j == 0:
                    if i < 3:
                        jointlist.append(tf.zeros([3, 1]))
                    else:
                        jointlist.append(joint_xyz[14])
                else:
                    k = j - 1
                    A = R[h, chain_idx[i][k]]
                    while k > 0:
                        k = k - 1
                        A = tf.matmul(R[h, chain_idx[i][k]], A)
                    jointlist.append(
                        tf.matmul(A, tf.reshape(bone_params[chain_idx[i][j]], [3, 1])) + joint_xyz[chain_idx[i][j - 1]])
                joint_xyz = tf.stack(jointlist)
        joints.append(tf.squeeze(joint_xyz))
    joints = tf.stack(joints)
    return joints

Answer 1

我遇到了类似的问题。我先转置然后重新塑造。它可能会对你有所帮助。我的任务是展开张量。我必须计算神经风格迁移中的内容成本。

 a_C_unrolled = tf.reshape(tf.transpose(a_C,perm=[0,3,1,2]),shape=[m,n_H*n_W,n_C])
    a_G_unrolled =tf.reshape( tf.transpose(a_G,perm=[0,3,1,2]),shape=[m,n_H*n_W,n_C])

“展开”的其他提示

To unroll the tensor, we want the shape to change from (m,nH,nW,nC)(m,nH,nW,nC) to (m,nH×nW,nC)(m,nH×nW,nC).

tf.reshape(tensor, shape) takes a list of integers that represent the desired output shape.

For the shape parameter, a -1 tells the function to choose the correct dimension size so that the output tensor still contains all the values of the original tensor.

So tf.reshape(a_C, shape=[m, n_H * n_W, n_C]) gives the same result as tf.reshape(a_C, shape=[m, -1, n_C]).

If you prefer to re-order the dimensions, you can use tf.transpose(tensor, perm), where perm is a list of integers containing the original index of the dimensions.

For example, tf.transpose(a_C, perm=[0,3,1,2]) changes the dimensions from (m,nH,nW,nC)(m,nH,nW,nC) to (m,nC,nH,nW)(m,nC,nH,nW).

There is more than one way to unroll the tensors.
Notice that it's not necessary to use tf.transpose to 'unroll' the tensors in this case but this is a useful function to practice and understand for other situations that you'll encounter.

Answer 2

我知道问题出在哪里

当您将这些维度放入列表对象时，它将 int32 转换为 Dimension(x) 您可以尝试使用 tensorflow 进行转换，但它对我不起作用，因为您再次需要将其转换为 Tensor ，所以您可以使用下面的代码在没有 tensorflow 的情况下进行转换

a_C_unrolled =  tf.reshape(a_C, shape=tf.constant([int(m), int(n_H * n_W), int(n_C)]))
a_G_unrolled =  tf.reshape(a_G, shape=tf.constant([int(m), int(n_H * n_W), int(n_C)]))

注意：-

你可以尝试使用 np.cast

a_C_unrolled= tf.reshape(a_C,shape=list(map(int,[n_C,n_H*n_W])))