![]() Slice index 10 of dimension 0 out of bounds. I’m not sure if my output shape is correct because when I try to fit my model, I get this error: InvalidArgumentError: Exception encountered when calling layer "keras_layer" (type KerasLayer). Qlayer = (qnode, weight_shapes, output_dim=(48,32)) Below is my implementation of one lstm: def lstmmodel(nfeatures, nhiddenunit, learningrate, p, recurrentp): model keras.Sequential() model. Return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2)), qml.expval(qml.PauliZ(3)), qml.expval(qml.PauliZ(4)),qml.expval(qml.PauliZ(5)), qml.expval(qml.PauliZ(6)), qml.expval(qml.PauliZ(7)), qml.expval(qml.PauliZ(8)), qml.expval(qml.PauliZ(9)) Model.add(tf.(units=36, return_sequences=True)) You can easily get the outputs of any layer by using: For all layers use this: from keras import backend as K inp model.input input placeholder outputs layer.output for layer in model.layers all layer outputs functors K.function(inp, K.learningphase(), out) for out in outputs evaluation functions Testing test np.random.random(inputshape)np. Unlike a function, though, layers maintain a state, updated when the layer receives data during. A layer consists of a tensor-in tensor-out computation function (the layer's call method) and some state, held in TensorFlow variables (the layer's weights ). Model.add(tf.(units=36, return_sequences=True, input_shape=(X_train.shape, X_train.shape))) Layers are the basic building blocks of neural networks in Keras. (1709, 48, 1) (1709, 24) (427, 48, 1) (427, 24) A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between. ![]() This is the shape of my data: print(X_train.shape, y_train.shape, X_test.shape, y_test.shape) name: sequential/keras_layer/strided_slice/ Slice index 10 of dimension 0 out of bounds. The Sequential Model API Multiple Inputs and Multiple Outputs Any of the individual layers has multiple inputs or multiple outputs You need to do layer. I tried adding forward pass through my model before printing the summary but I encounter this error: InvalidArgumentError: Exception encountered when calling layer "keras_layer" (type KerasLayer). To start constructing a model, you should first initialize a sequential model with the help of the kerasmodelsequential() function.
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