keras-layer

问题 I had trained a CNN model in keras with the following structure model_11 = Sequential() #Convolutional Layers model_11.add(Reshape((55, 1))) model_11.add(Conv1D(50, kernel_size=5, strides=1, padding="same", activation = 'relu')) model_11.add(Conv1D(24, kernel_size=4, strides=5, padding="same", activation = 'relu')) model_11.add(Conv1D(23, kernel_size=2, strides=1, padding="same", activation = 'relu')) #Dense Layers model_11.add(Flatten()) model_11.add(Dense(units=30, activation='relu')) model

问题 In the Keras docs for Embedding https://keras.io/layers/embeddings/, the explanation given for mask_zero is mask_zero: Whether or not the input value 0 is a special "padding" value that should be masked out. This is useful when using recurrent layers which may take variable length input. If this is True then all subsequent layers in the model need to support masking or an exception will be raised. If mask_zero is set to True, as a consequence, index 0 cannot be used in the vocabulary (input

问题 I have imported all necessary modules from keras into jupyter notebook from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.models import Sequential from keras.layers import Embedding, Dense, Dropout, Reshape, Merge, BatchNormalization, TimeDistributed, Lambda, Activation, LSTM, Flatten, Convolution1D, GRU, MaxPooling1D from keras.regularizers import l2 from keras.callbacks import Callback, ModelCheckpoint, EarlyStopping from keras

问题 this is the code after i try to save and load my model: model.save('path_to_my_model.h5') del model model = tf.keras.models.load_model('path_to_my_model.h5', custom_objects={'Wraparound2D': Wraparound2D}) import tensorflow.keras.backend as K inp = model.input # input placeholder outputs = [layer.output for layer in model.layers] # all layer outputs functor = K.function(inp, outputs) # evaluation function layer_outs = functor([X_test, 1.]) # Plot activations of different neurons in different

问题 I'm trying to create an activation function in Keras that can take in a parameter beta like so: from keras import backend as K from keras.utils.generic_utils import get_custom_objects from keras.layers import Activation class Swish(Activation): def __init__(self, activation, beta, **kwargs): super(Swish, self).__init__(activation, **kwargs) self.__name__ = 'swish' self.beta = beta def swish(x): return (K.sigmoid(beta*x) * x) get_custom_objects().update({'swish': Swish(swish, beta=1.)}) It

问题 I'm trying to create an activation function in Keras that can take in a parameter beta like so: from keras import backend as K from keras.utils.generic_utils import get_custom_objects from keras.layers import Activation class Swish(Activation): def __init__(self, activation, beta, **kwargs): super(Swish, self).__init__(activation, **kwargs) self.__name__ = 'swish' self.beta = beta def swish(x): return (K.sigmoid(beta*x) * x) get_custom_objects().update({'swish': Swish(swish, beta=1.)}) It

问题 I'm trying to build a CNN + RNN using a pre-trained model from tensorflow-hub: base_model = hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', input_shape=(244, 244, 3) base_model.trainable = False model = Sequential() model.add(TimeDistributed(base_model, input_shape=(15, 244, 244, 3))) model.add(LSTM(512)) model.add(Dense(256, activation='relu')) model.add(Dense(3, activation='softmax')) adam = Adam(learning_rate=learning_rate) model.compile(loss='categorical

问题 I'm trying to build a CNN + RNN using a pre-trained model from tensorflow-hub: base_model = hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', input_shape=(244, 244, 3) base_model.trainable = False model = Sequential() model.add(TimeDistributed(base_model, input_shape=(15, 244, 244, 3))) model.add(LSTM(512)) model.add(Dense(256, activation='relu')) model.add(Dense(3, activation='softmax')) adam = Adam(learning_rate=learning_rate) model.compile(loss='categorical

问题 I have compiled and trained a keras model with a custom optimizer. I saved the model but when I try to load the model, it throws an error stating ValueError: Unknown optimizer: MyOptimizer . I tried to pass MyOptimizer as a custom object something like : models.load_model('myModel.h5', custom_objects={'optimizer':MyOptimizer}) and it still throws an error. How do I load the model a keras model with custom Objects? 回答1: I ran into the same problem :) I made it work by loading the model with

问题 I have a general question. I am developing a new layer to incorporate into an autoencoder. To be more specific, the layer is something like the KCompetitive class over here. What I want is that I need to save the output of this layer in a variable let's call it previous_mat_values , and then pass it to that same layer in the next epoch as well. To put it another way, I want to be able to save the output of this layer of epoch 1 in one variable, and then in epoch 2 , again use that same matrix