keras-layer

I don't have problem in understanding output shape of a Dense layer followed by a Flatten layer. Output shape is in accordance of my understanding i.e (Batch size, unit). nn= keras.Sequential() nn.add(keras.layers.Conv2D(8,kernel_size=(2,2),input_shape=(4,5,1))) nn.add(keras.layers.Conv2D(1,kernel_size=(2,2))) nn.add(keras.layers.Flatten()) nn.add(keras.layers.Dense(5)) nn.add(keras.layers.Dense(1)) nn.summary() Output is: _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d

My model is a resnet-152 i wanna cutting it into two submodels and the problem is with the second one i can't figure out how to build a model from an intermediate layer to the output I tried this code from this response and it doesn't work for me here is my code: def getLayerIndexByName(model, layername): for idx, layer in enumerate(model.layers): if layer.name == layername: return idx idx = getLayerIndexByName(resnet, 'res3a_branch2a') input_shape = resnet.layers[idx].get_input_shape_at(0) # which is here in my case (None, 55, 55, 256) layer_input = Input(shape=input_shape[1:]) # as keras

I have a training set in a Pandas dataframe, and I pass this data frame into model.fit() with df.values . Here is some information about the df: df.values.shape # (981, 5) df.values[0] # array([163, 0.6, 83, 0.52, # array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, # 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, # 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, # 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, # 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, # 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0

I am doing image classification. Firstly I am feeding my images to my CNN model in Keras. I want to add new features at the output of Flatten layer in keras and then feed it to the dense layer(s). How do I write a code for it? Basically I am using Convolution for images and then at the end I want to add other features like Age Sex etc. max_pool_final = MaxPooling2D(pool_size=(2,2))(conv_final) flat = Flatten()(max_pool_final) dense = Dense(128)(flat) Before feeding flat as an input to the dense layer, I want to add a few features to flat. How do I do this? Thanks for help! You just have to use

This is my code: cnn_input = Input(shape=(cnn_max_length,)) emb_output = Embedding(num_chars + 1, output_dim=32, input_length=cnn_max_length, trainable=True)(cnn_input) output = TimeDistributed(Convolution1D(filters=128, kernel_size=4, activation='relu'))(emb_output) I want to train a character-level CNN sequence labeler and I keep receiving this error: Traceback (most recent call last): File "word_lstm_char_cnn.py", line 24, in <module> output = kl.TimeDistributed(kl.Convolution1D(filters=128, kernel_size=4, activation='relu'))(emb_output) File "/home/user/anaconda3/envs/thesisenv/lib/python3

I would like to implement Hierarchical Multiscale LSTM as a Keras layer. It was published here and implemented in tensorflow here . My understanding is that there's a way to wrap such a tensorflow object in Keras as a layer. I'm not sure how complicated it is but I think it's feasible. Can you help me how to do it? This is usually done by implementing a custom Layer . To be more specific, you should inherit from keras.engine.topology.layer and provide a custom implementation for the following methods (and place the TensorFlow code within them): build(input_shape) : this is where you will

I am using keras' pre-trained model and the error came up when trying to get predictions. I have the following code in flask server: from NeuralNetwork import * @app.route("/uploadMultipleImages", methods=["POST"]) def uploadMultipleImages(): uploaded_files = request.files.getlist("file[]") getPredictionfunction = preTrainedModel["VGG16"] for file in uploaded_files: path = os.path.join(STATIC_PATH, file.filename) result = getPredictionfunction(path) This is what I have in my NeuralNetwork.py file: vgg16 = VGG16(weights='imagenet', include_top=True) def getVGG16Prediction(img_path): model =

I am trying to implement roughly the following architecture in Keras (preferably) or Tensorflow. ___________ _________ _________ ________ ______ | Conv | | Max | | Dense | | | | | Input0--> | Layer 1 | --> | Pool 1 | --> | Layer | -->| | | | |_________| |________| |________| | Sum | | Out | | Layer |-->|_____| Input1 ----------- Converted to trainable weights-->| | |_______| |_______| In short, it is pretty much a model with two inputs, merged into one output using an Add([input0, input1]) layer. The trick is that one of the inputs must be seen as a variable = trainable weight. Keras layer Add