Created
September 22, 2016 18:46
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Keras SimpleRNN
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| batch_size = modelData.batch_size | |
| timesteps = modelData.X_train.shape[1] #xshape[1] | |
| input_dim = modelData.X_train.shape[2] #xshape[2] | |
| in_neurons = input_dim +7 | |
| hidden_layers = 1 | |
| hidden_neurons = in_neurons | |
| out_neurons = 3 | |
| ret_sequences = True | |
| rnn_activation = 'relu' | |
| dense_activation = 'linear' | |
| epochs = 111 | |
| # Input shape - 3D tensor with shape (nb_samples, timesteps, input_dim). | |
| model = Sequential() | |
| model.add(SimpleRNN( | |
| in_neurons, | |
| return_sequences = ret_sequences, | |
| stateful = False, | |
| activation = rnn_activation, | |
| batch_input_shape = (batch_size, timesteps, input_dim))) | |
| for _ in range(1, hidden_layers): | |
| model.add(SimpleRNN(hidden_neurons, | |
| return_sequences = True, | |
| stateful = False, | |
| activation = rnn_activation, | |
| ) ) | |
| # Not really a hidden layer but the easiest way to deal with 1 layer nets | |
| if hidden_layers > 0: | |
| model.add(SimpleRNN(out_neurons, | |
| return_sequences = False, | |
| stateful = False, | |
| activation = rnn_activation, | |
| ) ) | |
| model.add(Dense(1, activation = dense_activation)) | |
| model.compile(loss="mean_squared_error", optimizer="rmsprop") |
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