models/scm_model.py
def batched_mse(true_tensor, pred_tensor):
        """
        Calculate the mean square error on a batched tensor.
        The output has the same amount of dimensions as the input,
        but sets the last dimension size to 1, which contains the mean.
        :param true_tensor: The true values
        :param pred_tensor: The predicted values
        :return: The mean square error between the true and predicted tensors.
        """
        squared_difference = tf.squared_difference(true_tensor, pred_tensor)
        mse = tf.reduce_mean(squared_difference, axis=-1, keepdims=True)
        return mse

        Calculate the mean square error on a batched tensor.
        The output has the same amount of dimensions as the input,
        but sets the last dimension size to 1, which contains the mean.
        :param true_tensor: The true values
        :param pred_tensor: The predicted values
        :return: The mean square error between the true and predicted tensors.
        
Calculate the mean square error on a batched tensor.
The output has the same amount of dimensions as the input,
but sets the last dimension size to 1, which contains the mean.
Calculate the mean square error on a batched tensor . The output has the same amount of dimensions as the input but sets the last dimension size to 1 which contains the mean .
def batched_mse(true_tensor, pred_tensor):
        squared_difference = tf.squared_difference(true_tensor, pred_tensor)
        mse = tf.reduce_mean(squared_difference, axis=-1, keepdims=True)
        return mse
def batched_mse ( true_tensor , pred_tensor ) : squared_difference = tf . squared_difference ( true_tensor , pred_tensor ) mse = tf . reduce_mean ( squared_difference , axis = - 1 , keepdims = True ) return mse
Calculate the mean square error on a batched tensor.
Calculate the mean square error on a batched tensor .
"""
        Calculate the mean square error on a batched tensor.
        The output has the same amount of dimensions as the input,
        but sets the last dimension size to 1, which contains the mean.
        :param true_tensor: The true values
        :param pred_tensor: The predicted values
        :return: The mean square error between the true and predicted tensors.
        """
The mean square error between the true and predicted tensors.
The mean square error between the true and predicted tensors .
true_tensor
The true values
The true values
pred_tensor
The predicted values
The predicted values