WebNov 12, 2024 · You can use mask= in the call to heatmap() to choose which cells to show. Using two different masks for the diagonal and the off_diagonal cells, you can get the desired output: import numpy as np import seaborn as sns cf_matrix = np.array([[50, 2, 38], [7, 43, 32], [9, 4, 76]]) vmin = np.min(cf_matrix) vmax = np.max(cf_matrix) off_diag_mask … WebMar 8, 2024 · Note: Typically, anywhere a TensorFlow function expects a Tensor as input, the function will also accept anything that can be converted to a Tensor using tf.convert_to_tensor .
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WebOct 16, 2024 · I have obtained the tensor using the feature extraction method from a Keras Sequential model. The output was a tensor of the first mentioned type. However, when I … WebApr 20, 2024 · The function itself is ok. But When I want to use the function in one layer as the kernel_initializer, I encounter this error: TypeError: Cannot convert 0.0 to EagerTensor of dtype int32. My code is below: from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, … order 31 rules of the superior courts
TypeError: Cannot convert 0.0 to EagerTensor of dtype …
WebNov 20, 2024 · TypeError: Cannot convert provided value to EagerTensor. Provided value: 0.0 Requested dtype: int64 Ask Question Asked 3 years, 4 months ago Modified 2 years, 7 months ago Viewed 2k times -1 I am trying to train the transformer model available from the tensorflow official models. WebNov 27, 2024 · 1 Answer Sorted by: 0 you can cast a tensor from float32 to int32 either using tf.cast (given_tensor, tf.int32) or tf.to_int32 (given_tensor). Share Improve this … WebJul 28, 2024 · If any one is still facing this issue even after training and loading on the same version of Keras and Tensorflow, (which I did), just casting it manually to dtype float32 worked for me. here is a sample code snippet resembling my original problem (using the Functional API): order 3 volt electric motors and generators