For the same Binary Image Classification task, if in the final layer I use 1 node with Sigmoid activation function and binary_crossentropy loss function, then the training process goes through pretty smoothly (92% accuracy after 3 epochs on validation data). However, if I change the final layer to 2 nodes and use the Softmax activation function with sparse_categorical_crossentropy loss function, then the model doesn't seem to learn at all and stuck at 55% accuracy (the ratio of the negative class). …

I saw some examples of Autoencoders (on images) which use sigmoid as output layer and BinaryCrossentropy as loss function. The input to the Autoencoders is normalized [0..1] The sigmoid outputs values (value of each pixel of the image) [0..1] I tried to evaluate the output of BinaryCrossentropy and I'm confused. Assume for simplicity we have image [2x2] and we run Autoencoder and get 2 results. One result is close to the True value and the second is same as the …

You are training the following perceptron. The neuron in this perceptron has a sigmoid activation function. The sigmoid function is represented by the following equation: Using the update function for the weights: with a learning rate of η=1, and assuming that the current weights are w1 = 0.2 and w2 = 0.3, compute an iteration of the new weights by computing error and applying back to the inputs.

Sigmoid function predicts the probability value which is between 0 & 1. What is the formula in logistic regression that maps the predicted probabilities to either 1 or 0?

When mapping probabilities obtained in logistic regression to 0s & 1s using the sigmoid function, we use a threshold value of 0.5. If the predicted probability lies above 0.5, then it gets mapped to 1, if the predicted probability lies below 0.5, it gets mapped to 0. What if the predicted probability is exactly 0.5? What does 0.5 get mapped to?

I was trying to understand significance of S-shape of sigmoid / logistic function. The slope/derivative of sigmoid approaches zero for very large and very small input values. That is $σ'(z) ≈ 0$ for $z > 10$ or $z < -10$. So update to weights will be smaller. Whereas updates will be bigger when $z$ is not too big or too small. I dont get "why its significant to have "smaller updates when $z$ is too big and too small" and …

I have been training a neural network for a bounded regression and I am still in doubt for which activation function to use on the output layer. At first, I was convinced that a sigmoid would be the best option in my case because I need my output to be from 0 to 1, but cases near 0 and 1 were never predicted. So I have tried to use a hard sigmoid, but now I face (almost) the opposite problems, …

According to: https://stackoverflow.com/questions/65307833/why-is-the-decoder-in-an-autoencoder-uses-a-sigmoid-on-the-last-layer The last layer activation function contains sigmoid in order to the output to be in range [0, 1]. If the input to the autoencoder is normalized (each pixel between [0..1]), Can we change the activation function of the last layer from sigmoid to be something else ? Can we use no activation function at all ?

I am studying how to do text classification with multiple labels using tensorflow. Let's say my model is like: model = tf.keras.Sequential([ tf.keras.layers.Embedding(vocab_size, 50, weights=[embedding_matrix], trainable=False), tf.keras.layers.LSTM(128), tf.keras.layers.Dense(4, activation='sigmoid')]) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=tf.metrics.categorical_accuracy) I have 4 classes, the prediction function has the following results: pred=model.predict(X_test) pred array([[0.915674 , 0.4272042 , 0.69613266, 0.3520468 ], [0.915674 , 0.42720422, 0.69613266, 0.35204676], [0.915674 , 0.4272042 , 0.69613266, 0.3520468 ], [0.9156739 , 0.42720422, 0.69613266, 0.3520468 ], ...... You can see that every data has 4 prediction …