keras-layer

According to this the following copies weights from one model to another: target_model.set_weights(model.get_weights()) What about copying the weights of a specific layer, would this work? model_1.layers[0].set_weights(source_model.layers[0].get_weights()) model_2.layers[0].set_weights(source_model.layers[0].get_weights()) If I train model_1 and model_2 will they have separate weights? The documentation doesn't state whether if this get_weights makes a deep copy or not. If this doesn't work, how can this be achieved? Of course, it would be a copy of the weights. It does not make sense the

I'm running a web server using flask and the error comes up when I try to use vgg16, which is the global variable for keras' pre-trained VGG16 model. I have no idea why this error rises or whether it has anything to do with the Tensorflow backend. Here is my code: vgg16 = VGG16(weights='imagenet', include_top=True) def getVGG16Prediction(img_path): global vgg16 img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) pred = vgg16.predict(x) return x, sort(decode_predictions(pred, top=3)[0]) @app.route("

For each input I have, I have a 49x2 matrix associated. Here's what 1 input-output couple looks like input : [Car1, Car2, Car3 ..., Car118] output : [[Label1 Label2] [Label1 Label2] ... [Label1 Label2]] Where both Label1 and Label2 are LabelEncode and they have respectively 1200 and 1300 different classes. Just to make sure this is what we call a multi-output multi-class problem? I tried to flatten the output but I feared the model wouldn't understand that all similar Label share the same classes. Is there a Keras layer that handle output this peculiar array shape? Generally, multi-class

I have an 2D input (or 3D if one consider the number of samples) and I want to apply a keras layer that would take this input and outputs another 2D matrix. So, for example, if I have an input with size (ExV), the learning weight matrix would be (SxE) and the output (SxV). Can I do this with Dense layer? EDIT (Nassim request): The first layer is doing nothing. It's just to give an input to Lambda layer: from keras.models import Sequential from keras.layers.core import Reshape,Lambda from keras import backend as K from keras.models import Model input_sample = [ [[1,2,3,4,5],[6,7,8,9,10],[11,12

I’m working with Keras on a sentence similarity task (using the STS dataset) and am having problems merging the layers. The data consists of 1184 sentence pairs each scored between 0 and 5. Below are the shapes of my numpy arrays. I’ve padded each of the sentences to 50 words and run them through and embedding layer, using the glove embedding’s with 100 dimensions. When merging the two networks I'm getting an error.. Exception: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 arrays but instead

How to average weights in Keras models, when I train few models with the same architecture with different initialisations? Now my code looks something like this? datagen = ImageDataGenerator(rotation_range=15, width_shift_range=2.0/28, height_shift_range=2.0/28 ) epochs = 40 lr = (1.234e-3) optimizer = Adam(lr=lr) main_input = Input(shape= (28,28,1), name='main_input') sub_models = [] for i in range(5): x = Conv2D(32, kernel_size=(3,3), strides=1)(main_input) x = BatchNormalization()(x) x = Activation('relu')(x) x = MaxPool2D(pool_size=2)(x) x = Conv2D(64, kernel_size=(3,3), strides=1)(x) x =

I am trying to create my first ensemble models in keras. I have 3 input values and a single output value in my dataset. from keras.optimizers import SGD,Adam from keras.layers import Dense,Merge from keras.models import Sequential model1 = Sequential() model1.add(Dense(3, input_dim=3, activation='relu')) model1.add(Dense(2, activation='relu')) model1.add(Dense(2, activation='tanh')) model1.compile(loss='mse', optimizer='Adam', metrics=['accuracy']) model2 = Sequential() model2.add(Dense(3, input_dim=3, activation='linear')) model2.add(Dense(4, activation='tanh')) model2.add(Dense(3, activation