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Keras is a high-level API that can be used on top of TensorFlow, CNTK and Theano. … Consequently, Keras is designed for accelerating deep nets' designing. Keras is opensource like the underlying libraries it comes for and I guess its project is not for Google. …

I guess based on the answer here and the point referred here your problem is not from Keras. …

It uses Xavier initialization. If you don't initialize it, by default it uses this method for initialization. If you want to know how to perform that you can use here. In the first link at the end, li …

I guess you should change the following line to solve the problem: model.add(Conv2D(64, strides=5, kernel_size=EMBED_DIM, activation="relu", padding='valid')) instead use this code: model.add(Conv …

I suggest you using the pre-trained model and freezing all the convolution layers. You should just train the weights of your dense layers in this situation. So use transfer learning and freeze the con …

If I get the point right based on the title of the question, in your code you are not making multi layer perceptron. You have tried to make somehow a convolutional network. In MLPs you just have to st …

A good reason to choose Keras is that you could use TensorFlow backend without actually learning it. … Plus Keras tends to wrap up the model deeply, so you don't necessarily need to consider the backend to be Theano or TF, which is a big advantage of Keras. …

=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None) fit method used in Keras …

We use freezing to employ transfer learning. Deep learning has a great hunger for data. In some tasks you may not have so much data, but there may already be a pre-trained network that can be helpful. …

As you can see here Keras models contain predict method but they do not have the method predict_proba() you have specified and they actually do not need it. … For more information as stated here in the recent version of keras, predict and predict_proba are the same i.e. both give probabilities. To get the class labels use predict_classes. …

Is there any available Keras code for my need? I've seen here and also here but they represent a high level abstraction and don't contain appropriate detailed answer. …

I want to use a pre-trained VGG16 in keras. My question is simple. Should I normalize the input image before predicting its label? …

Recently I was looking the code of optimizers in Keras and I found that as the following code: if self.initial_decay > 0: lr *= (1. / (1. + self.decay * K.cast(self.iterations, …

That's true. For understanding how many correct decision your classifier has made, confusion matrix can be used. The main diagonal illustrates that. It depicts how many data samples are correctly clas …

You may not be very familiar with deep learning. Each kind of network is used for a special kind of task, you cannot just stack LSTMs, GRUs, dense layers and other stuff without supervision. If you ha …