keras-layer

I am trying a simple document classification using sentence embeddings in keras. I know how to feed word vectors to a network, but I have problems using sentence embeddings. In my case, I have a simple representation of sentences (adding the word vectors along the axis, for example np.sum(sequences, axis=0) ). My question is, what should I replace the Embedding layer with in the code below to feed sentence embeddings instead? model = Sequential() model.add(Embedding(len(embedding_weights), len(embedding_weights[0]), weights=[embedding_weights], mask_zero=True, input_length=MAX_SEQUENCE_LENGTH,

I am using Keras with tensorflow backend and I am curious whether it is possible to skip a layer during backpropagation but have it execute in the forward pass. So here is what I mean Lambda (lambda x: a(x)) I want to apply a to x in the forward pass but I do not want a to be included in the derivation when the backprop takes place. I was trying to find a solution bit I could not find anything. Can somebody help me out here? UPDATE 2 In addition to tf.py_func , there is now an official guide on how to add a custom op . UPDATE See this question for an example of writing a custom op with

I have a saved a model using model.save() . I'm trying to reload the model and add a few layers and tune some hyper-parameters, however, it throws the AttributeError. Model is loaded using load_model() . I guess I'm missing understanding how to add layers to saved layers. If someone can guide me here, it will be great. I'm a novice to deep learning and using keras, so probably my request would be silly. Snippet: prev_model = load_model('final_model.h5') # loading the previously saved model. prev_model.add(Dense(256,activation='relu')) prev_model.add(Dropout(0.5)) prev_model.add(Dense(1

How can I add a resizing layer to model = Sequential() using model.add(...) To resize an image from shape (160, 320, 3) to (224,224,3) ? Normally you would use the Reshape layer for this: model.add(Reshape((224,224,3), input_shape=(160,320,3)) but since your target dimensions don't allow to hold all the data from the input dimensions ( 224*224 != 160*320 ), this won't work. You can only use Reshape if the number of elements does not change. If you are fine with losing some data in your image, you can specify your own lossy reshape: model.add(Reshape(-1,3), input_shape=(160,320,3)) model.add

I am trying to implement a denoising autoencoder with an LSTM layer in between. The architecture goes following. FC layer -> FC layer -> LSTM cell -> FC layer -> FC layer. I am unable to understand how my input dimension should be to implement this architecture? I tried the following code batch_size = 1 model = Sequential() model.add(Dense(5, input_shape=(1,))) model.add(Dense(10)) model.add(LSTM(32)) model.add(Dropout(0.3)) model.add(Dense(5)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, nb_epoch=100, batch_size=batch_size, verbose=2