It is sometimes faster to convert the gradient to a `Tensor`, so as to get the cheaper gradient for `tf.concat`. To do this, replace `tf.concat(x)` with `convert_gradient_to_tensor(tf.concat(x))`. Args: x: A `Tensor`. Returns: The input `Tensor`. """ return x ...
def file_to_dataset(filepath, N, vectorizer): #convert file to dataset, returns X and Y tensors of integer indexes describing the N words (X) leading up to (Y) f = open(filepath) lines = f.readlines() X = [] Y = [] line_index = 0 for line in lines: if line_index % 100...
batch_evaluate_objective):"""Evaluates the objective function at the specified initial simplex."""initial_simplex = tf.convert_to_tensor(initial_simplex)# If d is the dimension of the problem, the number of vertices in the# simplex should be d+1. From this, we can infer the number of d...
array_ops.shape(y), constant_op.constant(1, dtype=y.dtype))continueify.dtype.is_floatingory.dtype.is_integer:ifnotgrad_y.dtype.is_floatingandnotgrad_y.dtype.is_integer:raiseTypeError("Gradient type %s generated for real or ""integer-valued tensor %s with type %s m...
so the exception raises while trying to convert this label to tensors using below code. batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64) The labels are always integer values, so this got converted to float values in the label padding operation below. ...
This means that the trace might not generalize to other inputs! ret = [int(r) for r in ret] /home/keli/下载/mmdeploy/mmdeploy/pytorch/functions/getattribute.py:19: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data ...
we have created a tensor and the name of the tensor is population_of_UnitedStates by using thetf.constant()function, and within this function, we have assigned the string values to it. Now we want to convert that string value to an integer. For this, we used thetf.strings.to_number(...
then `partitioned_dim_sizes[d]` must be an integer vector, specifying the size of each slice across dimension `d`. inner_dim_sizes: A 1-D integer `Tensor`, whose length is equal to the number of inner dimensions. `inner_dim_sizes[n]` is the size of all ...
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float) I wrotea bookin which I share everything I know about how to become a better, more efficient programmer. You can use the search field on myHome Pageto filter through all of my articles. ...
tf.convert_to_tensor( value, dtype=None, dtype_hint=None, name=None ) The parameters that it accepts are: value:This parameter indicates the object (lists, strings, or numpy arrays) you want to convert into tensors. dtype:It is an optional parameter representing the data type of the retu...