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26.2k members in the tensorflow community. TensorFlow is an open source Machine Intelligence library for numerical computation using Neural Networks.

I try to use Keras Tuner for hyperparameter optimisation: import keras from kerastuner import HyperModel from kerastuner.tuners import Hyperband input_shape = (1, 28, 28) num_classes = …

06.11.2019 · version: python 3.6.9 OS: windows10 IDE: anaconda, spyder (executing locally) also installed tensorflow by creating new environment in anaconda (without using pip) tensorflow version 2.0.0 code sho...

I have an AttributeError: module 'tensorflow_core.python.keras.api._v2.keras.losses' has no attribute 'softmax_cross_entropy' error when using ...

25.05.2021 · I am a univ student. I am working on my graduation work using the EfficientDet model. First of all, I am using Efficientdet model for fine tuning my custom dataset. python = 3.7 tensorflow = 2.5

27.11.2019 · Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

09.03.2021 · AttributeError: module 'tensorflow._api.v1.keras.losses' has no attribute 'SparseCategoricalCrossent报错如下解决方法报错如下解决方法将model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

12.06.2020 · Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

AttributeError: module 'tensorflow' has no attribute 'Session'. 不用担心，你的tensorflow已经安装成功，原因是你安装的tensorflow是2.0以上版本. sess = tf.Session () 代码已经修改为. sess = tf.compat.v1.Session () 当然，如果你觉得不习惯的话，还有另一种方法，将你的tensorflow从2.0以上 …

I am using Huber loss implementation in tf.keras in tensorflow 1.14.0 as ... the error AttributeError: module 'tensorflow.python.keras.api.

Replacing tf.keras.losses.Reduction. with tf.compat.v1.losses.Reduction. solved the issue for me in TensorFlow 1.14.0.

If reduction is NONE , this has shape [batch_size, d0, .. dN-1] ; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 ...