TimeDistributedDense applies a same dense to every time step during GRU/LSTM Cell unrolling. So the error function will be between predicted label sequence and the actual label sequence. (Which is normally the requirement for sequence to sequence labeling problems). However, with return_sequences=False, Dense layer is applied only once at the last cell. This is normally … Read more
I solved the problem by making input size: (95000,360,1) and output size: (95000,22) and changed the input shape to (360,1) in the code where model is defined: model = Sequential() model.add(LSTM(22, input_shape=(360,1))) model.add(Dense(22, activation=’softmax’)) model.compile(loss=”categorical_crossentropy”, optimizer=”adam”, metrics=[‘accuracy’]) print(model.summary()) model.fit(ml2_train_input, ml2_train_output_enc, epochs=2, batch_size=500)
Here I’m answering to OP’s topic question rather than his exact problem. I’m doing this as the question shows up in the top when I google the topic problem. You can implement a custom metric in two ways. As mentioned in Keras docu. import keras.backend as K def mean_pred(y_true, y_pred): return K.mean(y_pred) model.compile(optimizer=”sgd”, loss=”binary_crossentropy”, metrics=[‘accuracy’, … Read more
Yes. Just change your input shape to shape=(n_channels, None, None). Where n_channels is the number of channels in your input image. I’m using Theano backend though, so if you are using tensorflow you might have to change it to (None,None,n_channels) You should use: input_shape=(1, None, None) None in a shape denotes a variable dimension. Note … Read more
from keras.models import Model from keras.layers import * #inp is a “tensor”, that can be passed when calling other layers to produce an output inp = Input((10,)) #supposing you have ten numeric values as input #here, SomeLayer() is defining a layer, #and calling it with (inp) produces the output tensor x x = SomeLayer(blablabla)(inp) x … Read more
If you want to get weights and biases of all layers, you can simply use: for layer in model.layers: print(layer.get_config(), layer.get_weights()) This will print all information that’s relevant. If you want the weights directly returned as numpy arrays, you can use: first_layer_weights = model.layers[0].get_weights()[0] first_layer_biases = model.layers[0].get_weights()[1] second_layer_weights = model.layers[1].get_weights()[0] second_layer_biases = model.layers[1].get_weights()[1] etc.
You will need to update your LD_LIBRARY_PATH, so that it points to the /usr/local/cuda-9.0/lib64. Add the following line to your .bashrc file (or any other terminal you use) export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-9.0/lib64/
Save the initial weights right after compiling the model but before training it: model.save_weights(‘model.h5’) and then after training, “reset” the model by reloading the initial weights: model.load_weights(‘model.h5’) This gives you an apples to apples model to compare different data sets and should be quicker than recompiling the entire model.
This function was removed in TensorFlow version 2.6. According to the keras in rstudio reference update to predict_x=model.predict(X_test) classes_x=np.argmax(predict_x,axis=1) Or use TensorFlow 2.5.x . If you are using TensorFlow version 2.5, you will receive the following warning: tensorflow\python\keras\engine\sequential.py:455: UserWarning: model.predict_classes() is deprecated and will be removed after 2021-01-01. Please use instead:* np.argmax(model.predict(x), axis=-1), if your … Read more
Just to answer this question in a little more detail, and as Pavel said, Batch Normalization is just another layer, so you can use it as such to create your desired network architecture. The general use case is to use BN between the linear and non-linear layers in your network, because it normalizes the input … Read more