I have CNN model build in tensorflow which outputs grid with shape (batch_size,H,W,1).
i perform sigmoid activation function on each cell in the grid.
my y_true is tensor with [ColIndex,RowIndex,Outcome], meaning for every training instance i have exactly 1 cell with an outcome value.

I have CNN model build in tensorflow which outputs grid with shape (batch_size,H,W,1).
i perform sigmoid activation function on each cell in the grid.
my y_true is tensor with [ColIndex,RowIndex,Outcome], meaning for every training instance i have exactly 1 cell with an outcome value.

I have CNN model build in tensorflow which outputs grid with shape (batch_size,H,W,1).
i perform sigmoid activation function on each cell in the grid.
my y_true is tensor with [ColIndex,RowIndex,Outcome], meaning for every training instance i have exactly 1 cell with an outcome value.

import numpy as np import tensorflow as tp # Importing imdb dataset from tensorflow.keras.datasets import imdb from tensorflow.keras.preprocessing import sequence # The sequence module in tensorflow.keras.preprocessing provides utilities for preprocessing sequence data, such as text or time-series data. from tensorflow.keras.models import Sequential # The sequence module in tensorflow.keras.preprocessing provides utilities for preprocessing sequence data, such […]

import numpy as np import tensorflow as tp # Importing imdb dataset from tensorflow.keras.datasets import imdb from tensorflow.keras.preprocessing import sequence # The sequence module in tensorflow.keras.preprocessing provides utilities for preprocessing sequence data, such as text or time-series data. from tensorflow.keras.models import Sequential # The sequence module in tensorflow.keras.preprocessing provides utilities for preprocessing sequence data, such […]

import numpy as np import tensorflow as tp # Importing imdb dataset from tensorflow.keras.datasets import imdb from tensorflow.keras.preprocessing import sequence # The sequence module in tensorflow.keras.preprocessing provides utilities for preprocessing sequence data, such as text or time-series data. from tensorflow.keras.models import Sequential # The sequence module in tensorflow.keras.preprocessing provides utilities for preprocessing sequence data, such […]

I met an issue during deep learning model building and training process when I worked with Jupyter Notebook in VS code.
I tried to train the deep learning model that I built using Tensorflow using model.fit method, but the issue happened.

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Recientemente estudiando Tensorflow encontré que es posible tener múltiples entradas (y salidas) usando capas compartidas como Add, Substract o Concatenate.

I am encountering an issue while training a CNN model using Tensorflow for a multiclass classification task. When attempting to fit the model, I am getting the following traceback error: