Loss_2 = loss_taking_logits(y_true, nn_output_before_softmax) loss_taking_logits = tf.(from_logits=True) Cross-Entropy Loss: Make Predictions with Confidence Have you ever wondered what happens under the hood when you train a neural network You’ll run the gradient descent optimization algorithm to find the optimal parameters (weights and biases) of the network. Here you are letting TensorFlow perform the softmax operation for you. This means that whatever inputs you are providing to the loss function is not scaled (means inputs are just the number from -inf to +inf and not the probabilities). Tf.Tensor(0.25469932, shape=(), dtype=float32)Ģ) One is not using the softmax function separately and wants to include it in the calculation of the loss function. This question does not appear to be about programming within the scope defined in the help center. This question does not meet Stack Overflow guidelines. Loss_1 = loss_taking_prob(y_true, nn_output_after_softmax) What is cross-entropy closed Ask Question Asked 6 years, 5 months ago Modified 2 years, 1 month ago Viewed 65k times 115 Closed. Loss_taking_prob = tf.(from_logits=False) So here TensorFlow is assuming that whatever the input that you will be feeding to the loss function are the probabilities, so no need to apply the softmax function. When one is explicitly using softmax (or sigmoid) function, then, for the classification task, then there is a default option in TensorFlow loss function i.e. One is not using softmax function separately and wants to include in the calculation of loss functionġ) One is explicitly using the softmax (or sigmoid) function One is explicitly using the softmax (or sigmoid) function This video discusses the Cross Entropy Loss and provides an intuitive interpretation of the loss function through a simple classification set up. # output converted into softmax after appling softmax Nn_output_after_softmax = tf.nn.softmax(nn_output_before_softmax) # converting output of last layer of NN into probabilities by applying softmax Assume your Neural Network is producing output, then you convert that output into probabilities using softmax function and calculate loss using a cross-entropy loss function # output produced by the last layer of NN Let us take an example where our network produced the output for the classification task. Now, this output layer will get compared in cross-entropy loss function with the true label. yn one wants to produce each output with some probability.

So the sequence is Neural Network ⇒ Last layer output ⇒ Softmax or Sigmoid function ⇒ Probability of each class.įor example in the case of a multi-class classification problem, where output can be y1, y2. Just look at the image below, the last layer of the network(just before softmax function) Remember in case of classification problem, at the end of the prediction, usually one wants to produce output in terms of probabilities. You can change the Activation layer and from_logits of CategoricalCrossentropy and see what i said.īy default, all of the loss function implemented in Tensorflow for classification problem uses from_logits=False. You can checkout and run (can run on cpu). Why? Is there something wrong for my usage? Y.append(seg_labels.reshape(width*height, n_classes)) Seg_labels = np.zeros((height, width, n_classes)) Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. My groundtruth dataset is generated like this: X =

The training will converge for one training image. Īnd then using loss = tf.(from_logits=True) There is a key difference between the two types of loss: Accuracy measures whether you get the prediction right Cross entropy measures how confident you are about a prediction For example, if an image of a cat is passed into two models. The training will not converge even for only one training image.īut if I do not set the Softmax Activation for last layer like this. A model can overfit to cross entropy loss without over overfitting to accuracy. Ĭonv9 = Conv2D(n_classes, (3,3), padding = 'same')(conv9)Īnd then using loss = tf.(from_logits=False) What is Cross Entropy Loss Cross entropy loss is a metric used in machine learning to measure how well a classification model performs. I am doing the image semantic segmentation job with unet, if I set the Softmax Activation for last layer like this.