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Tuning number of hidden layers in Keras

By : Tony
Date : October 16 2020, 06:10 PM
I wish this help you I'm just trying to explore keras and tensorflow with the famous MNIST dataset. I already applied some basic neural networks, but when it comes to tuning some hyperparameters, especially the number of layers, thanks to the sklearn wrapper GridSearchCV, I get the error below: , You should add:
code :
for i in range(int(hidden_layers)):
    # Add one hidden layer
    model.add(Dense(16, activation=activation))
params_grid={"hidden_layers": [3]}

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Which layers should I freeze for fine tuning a resnet model on keras?

By : Franco Narciso Romer
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I think that there is no a state of the art strategy for this but I may share you my thoughts on this topic (names of layers are similar to these presented here:

hpneural parameter tuning - variable number of hidden layers (in do loop)

By : Tyrnavan Janne
Date : March 29 2020, 07:55 AM
this one helps. In your second macro use a %do loop to emit the desired source code. In the first macro use a macro invocation instead of a macro variable resolution
code :
%macro hidden_layers (layers=, neurons=);

  %local i;
  %do i = 1 %to &layers;
    hidden &neurons;   /* macro will emit this source code &layer times */

            target MSRP / level=int;
            hidden &neurons.;
            train outmodel=model_neural_network maxiter=1000
            target MSRP / level=int;
            %hidden_layers (3, &neurons)
            train outmodel=model_neural_network maxiter=1000
%macro build_predictions
  %do neurons = 1 % to 10;
        proc hpneural data=train;
            input Make -- Horsepower / level=nom;
            target MSRP / level=int;

            %local index;
            %do index = 1 %to 3; hidden &neurons.; %end;

            train outmodel=model_neural_network maxiter=1000;
    %end; %* neurons loop;

Setting the hidden state for each minibatch with different hidden sizes and multiple LSTM layers in Keras

By : Patrick
Date : March 29 2020, 07:55 AM
To fix the issue you can do States in LSTM
An LSTM is made up of gates which calculate the cell state and hidden state.
code :
batch_size = 2
num_steps = 5
num_input = num_output = 1
hidden_size = 8

inputs = Input(batch_shape=(batch_size,num_steps, num_input))
lstm, state_h, state_c = LSTM(hidden_size, return_state=True, return_sequences=True)(inputs)
model = Model(inputs=inputs, outputs=[state_h, state_c])

print (model.predict(np.zeros((batch_size, num_steps, num_input))))
print (model.layers[1].cell.state_size)
 print (model.layers[0].states[0], hex(id(model.layers[0].states[0])))
 model.layers[0].states[0]= K.variable(np.random.randn(10,2))
 print (model.layers[0].states[0], hex(id(model.layers[0].states[0])))
<tf.Variable 'lstm_18/Variable:0' shape=(10, 8) dtype=float32_ref> 0x7f8812e6ee10
<tf.Variable 'Variable_2:0' shape=(10, 2) dtype=float32_ref> 0x7f881269afd0
 print (model.layers[0].states[0], hex(id(model.layers[0].states[0])))
 K.set_value(model.layers[0].states[0], np.random.randn(10,8))
 print (model.layers[0].states[0], hex(id(model.layers[0].states[0])))
<tf.Variable 'lstm_20/Variable:0' shape=(10, 8) dtype=float32_ref> 0x7f881138eb70
<tf.Variable 'lstm_20/Variable:0' shape=(10, 8) dtype=float32_ref> 0x7f881138eb70
K.set_value(model.layers[0].states[0], np.random.randn(10,2))

Grid Search the number of hidden layers with keras

By : sameera
Date : March 29 2020, 07:55 AM
Hope this helps If you want to make the number of hidden layers a hyperparameter you have to add it as parameter to your KerasClassifier build_fn like:
code :
def create_model(optimizer='adam', activation = 'sigmoid', hidden_layers=1):
  # Initialize the constructor
  model = Sequential()
  # Add an input layer
  model.add(Dense(5, activation=activation, input_shape=(5,)))

  for i in range(hidden_layers):
      # Add one hidden layer
      model.add(Dense(8, activation=activation))

  # Add an output layer 
  model.add(Dense(1, activation=activation))
  #compile model
  model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=
  return model

Neural network tuning number of hidden layers using grid search

By : Michael
Date : March 29 2020, 07:55 AM
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