labels = [-0.6, -0.04, -0.001, .3, .4, 1.0] labels = tf.convert_to_tensor(labels, dtype=tf.float32) labels = tf.convert_to_tensor(labels, dtype=tf.float64) Describe the expected behavior It should convert it. Standalone code to reproduce the issue Provide a reproducible test case...
convert_to_tensor(arg, dtype=tf.float32) return arg # The following calls are equivalent. value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]])) print(value_1) tf.Tensor( [[1. 2.] [3. 4.]], shape=(2, 2), dtype=float32) value_2 = my_func([[1.0, 2.0], [3.0, ...
tensor = tf.convert_to_tensor(np.array([0, 1, 2, 3, 4]), dtype=tf.int64) sess.run(tensor) # array([0, 1, 2, 3, 4]) # Use tf.cast() tensor_float = tf.cast(tensor, tf.float32) sess.run(tensor_float) # array([ 0., 1., 2., 3., 4.], dtype=float32) # Use tf...
tf.Tensor( [[1. 2.] [3. 4.]], shape=(2, 2), dtype=float32) tf.Tensor( [[1. 2.] [3. 4.]], shape=(2, 2), dtype=float32) 1. 2. 3. 4. 5. 6. 7. 8. 9.
TypeError: Cannot convert Type TensorType(float64, matrix) (of Variable <TensorType(float64, matrix)>) into Type TensorType(float32, matrix). There is no bugs in the codes because i can run it correctly in my computer. But why it has such a problem in another compute...
TypeError:Fetch argument2.3025854has invalid type <class'numpy.float32'>, must be a string or Tensor. (Can not convert a float32 into a Tensor or Operation.) 如其意,类型错误:不能将一个浮动32转换为一个张量或操作。也就是在计算图实际运算的时候发生的错误,错误为数据类型错误,将应该是传入的...
Tensor("add_3:0", shape=(2,), dtype=float32) 1. 张量的属性值主要有三个:名字(name)、维度(shape)和类型(type) name 张量的唯一标识符; 给出了张量是如何计算出来的。 张量和计算图上的每一个节点所有代表的结果是对应的。张量的命名:node:src_output。
Doesn't that still return a tensor? I would like to get a float value instead of a tensor, which I can append to a list (initiated by list = []) . I am using inside a tf.function, maybe that changes things? 👍2agg-shambhavi and cgebbe reacted with thumbs up emoji ...
遇到这种情况可能是你的程序中有和你定义的tensor 变量重名的其他变量名字,jishi在for循环中使用了这个名字的作为临时变量也不行.tenor 变量很娇气.坑了我一晚上的时间. 比如:x = tf.placeholder(tf.float32,[None,512]) 那么在程序中就不能使用x作为其他变量名,jishi临时的也不行.(我就在for循环中使用了x所...
x_int8 = tf.convert_to_tensor(x, dtype=tf.int8) tf.image.convert_image_dtype(x_int8, dtype=tf.float16, saturate=False) <tf.Tensor:shape=(2,2,3), dtype=float16, numpy= array([[[0.00787,0.01575,0.02362], [0.0315,0.03937,0.04724]], ...