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from keras.models import Sequential |
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from keras.layers import Dense, Activation, Convolution1D, Flatten, MaxPooling1D, Lambda, Convolution2D, Flatten, Reshape, LSTM, Dropout, TimeDistributed, Permute, BatchNormalization |
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from keras.regularizers import l2 |
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from keras.optimizers import Adam |
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import numpy as np |
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def generate_models( |
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x_shape, number_of_classes, number_of_models=5, model_type=None, |
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cnn_min_layers=1, cnn_max_layers=10, |
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cnn_min_filters=10, cnn_max_filters=100, |
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cnn_min_fc_nodes=10, cnn_max_fc_nodes=100, |
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deepconvlstm_min_conv_layers=1, deepconvlstm_max_conv_layers=10, |
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deepconvlstm_min_conv_filters=10, deepconvlstm_max_conv_filters=100, |
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deepconvlstm_min_lstm_layers=1, deepconvlstm_max_lstm_layers=5, |
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deepconvlstm_min_lstm_dims=10, deepconvlstm_max_lstm_dims=100, |
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low_lr=1, high_lr=4, low_reg=1, high_reg=4 |
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): |
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""" |
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Generate one or multiple Keras models with random hyperparameters. |
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Parameters |
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---------- |
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x_shape |
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Shape of the input dataset: (num_samples, num_timesteps, num_channels) |
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number_of_classes |
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Number of classes for classification task |
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number_of_models |
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Number of models to generate |
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model_type : str (optional) |
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Type of model to build: 'CNN' or 'DeepConvLSTM'. Default option None generates both models. |
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cnn_min_layers : int |
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minimum of Conv layers in CNN model |
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cnn_max_layers : int |
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maximum of Conv layers in CNN model |
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cnn_min_filters : int |
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minimum number of filters per Conv layer in CNN model |
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cnn_max_filters : int |
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maximum number of filters per Conv layer in CNN model |
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cnn_min_fc_nodes : int |
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minimum number of hidden nodes per Dense layer in CNN model |
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cnn_max_fc_nodes : int |
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maximum number of hidden nodes per Dense layer in CNN model |
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deepconvlstm_min_conv_layers : int |
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minimum number of Conv layers in DeepConvLSTM model |
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deepconvlstm_max_conv_layers : int |
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maximum number of Conv layers in DeepConvLSTM model |
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deepconvlstm_min_conv_filters : int |
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minimum number of filters per Conv layer in DeepConvLSTM model |
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deepconvlstm_max_conv_filters : int |
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maximum number of filters per Conv layer in DeepConvLSTM model |
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deepconvlstm_min_lstm_layers : int |
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minimum number of Conv layers in DeepConvLSTM model |
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deepconvlstm_max_lstm_layers : int |
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maximum number of Conv layers in DeepConvLSTM model |
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deepconvlstm_min_lstm_dims : int |
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minimum number of hidden nodes per LSTM layer in DeepConvLSTM model |
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deepconvlstm_max_lstm_dims : int |
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maximum number of hidden nodes per LSTM layer in DeepConvLSTM model |
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low_lr : float |
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minimum of log range for learning rate: learning rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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high_lr : float |
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maximum of log range for learning rate: learning rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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low_reg : float |
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minimum of log range for regularization rate: regularization rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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high_reg : float |
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maximum of log range for regularization rate: regularization rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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Returns |
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------- |
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List of compiled models |
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""" |
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models = [] |
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for _ in range(0, number_of_models): |
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if model_type is None: # random model choice: |
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current_model_type = 'CNN' if np.random.random( |
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) < 0.5 else 'DeepConvLSTM' |
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else: # user-defined model choice: |
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current_model_type = model_type |
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generate_model = None |
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if current_model_type == 'CNN': |
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generate_model = generate_CNN_model # object is a function |
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hyperparameters = generate_CNN_hyperparameter_set( |
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min_layers=cnn_min_layers, max_layers=cnn_max_layers, |
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min_filters=cnn_min_filters, max_filters=cnn_max_filters, |
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min_fc_nodes=cnn_min_fc_nodes, max_fc_nodes=cnn_max_fc_nodes, |
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low_lr=low_lr, high_lr=high_lr, low_reg=low_reg, high_reg=high_reg) |
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if current_model_type == 'DeepConvLSTM': |
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generate_model = generate_DeepConvLSTM_model # object is a function |
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hyperparameters = generate_DeepConvLSTM_hyperparameter_set( |
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min_conv_layers=deepconvlstm_min_conv_layers, max_conv_layers=deepconvlstm_max_conv_layers, |
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min_conv_filters=deepconvlstm_min_conv_filters, max_conv_filters=deepconvlstm_max_conv_filters, |
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min_lstm_layers=deepconvlstm_min_lstm_layers, max_lstm_layers=deepconvlstm_max_lstm_layers, |
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min_lstm_dims=deepconvlstm_min_lstm_dims, max_lstm_dims=deepconvlstm_max_lstm_dims, |
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low_lr=low_lr, high_lr=high_lr, low_reg=low_reg, high_reg=high_reg) |
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models.append( |
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(generate_model(x_shape, number_of_classes, **hyperparameters), hyperparameters, current_model_type)) |
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return models |
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def generate_DeepConvLSTM_model( |
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x_shape, class_number, filters, lstm_dims, learning_rate=0.01, |
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regularization_rate=0.01): |
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""" |
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Generate a model with convolution and LSTM layers. |
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See Ordonez et al., 2016, http://dx.doi.org/10.3390/s16010115 |
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Parameters |
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---------- |
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x_shape : tuple |
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Shape of the input dataset: (num_samples, num_timesteps, num_channels) |
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class_number : int |
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Number of classes for classification task |
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filters : list of ints |
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number of filters for each convolutional layer |
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lstm_dims : list of ints |
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number of hidden nodes for each LSTM layer |
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learning_rate : float |
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regularization_rate : float |
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Returns |
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------- |
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The compiled Keras model |
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""" |
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dim_length = x_shape[1] # number of samples in a time series |
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dim_channels = x_shape[2] # number of channels |
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output_dim = class_number # number of classes |
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weightinit = 'lecun_uniform' # weight initialization |
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model = Sequential() # initialize model |
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model.add(BatchNormalization(input_shape=(dim_length, dim_channels))) |
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# reshape a 2 dimensional array per file/person/object into a |
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# 3 dimensional array |
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model.add( |
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Reshape(target_shape=(1, dim_length, dim_channels))) |
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for filt in filters: |
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# filt: number of filters used in a layer |
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# filters: vector of filt values |
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model.add( |
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Convolution2D(filt, nb_row=3, nb_col=1, border_mode='same', |
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W_regularizer=l2(regularization_rate), init=weightinit)) |
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model.add(Activation('relu')) |
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# reshape 3 dimensional array back into a 2 dimensional array, |
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# but now with more dept as we have the the filters for each channel |
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model.add(Reshape(target_shape=(dim_length, filters[-1] * dim_channels))) |
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for lstm_dim in lstm_dims: |
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model.add(LSTM(output_dim=lstm_dim, return_sequences=True, |
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activation='tanh')) |
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model.add(Dropout(0.5)) # dropout before the dense layer |
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# set up final dense layer such that every timestamp is given one |
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# classification |
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model.add( |
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TimeDistributed(Dense(output_dim, W_regularizer=l2(regularization_rate)))) |
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model.add(Activation("softmax")) |
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# Final classification layer - per timestep |
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model.add(Lambda(lambda x: x[:, -1, :], output_shape=[output_dim])) |
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model.compile(loss='categorical_crossentropy', |
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optimizer=Adam(lr=learning_rate), |
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metrics=['accuracy']) |
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return model |
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def generate_CNN_model(x_shape, class_number, filters, fc_hidden_nodes, |
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learning_rate=0.01, regularization_rate=0.01): |
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""" |
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Generate a convolutional neural network (CNN) model. |
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The compiled Keras model is returned. |
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Parameters |
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---------- |
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x_shape : tuple |
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Shape of the input dataset: (num_samples, num_timesteps, num_channels) |
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class_number : int |
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Number of classes for classification task |
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filters : list of ints |
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number of filters for each convolutional layer |
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fc_hidden_nodes : int |
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number of hidden nodes for the hidden dense layer |
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learning_rate : float |
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regularization_rate : float |
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Returns |
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------- |
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The compiled Keras model |
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""" |
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dim_length = x_shape[1] # number of samples in a time series |
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dim_channels = x_shape[2] # number of channels |
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outputdim = class_number # number of classes |
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weightinit = 'lecun_uniform' # weight initialization |
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model = Sequential() |
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model.add(BatchNormalization(input_shape=(dim_length, dim_channels), mode=0, axis=2)) |
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for filter_number in filters: |
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model.add(Convolution1D(filter_number, 3, border_mode='same', |
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W_regularizer=l2(regularization_rate), init=weightinit)) |
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model.add(BatchNormalization()) |
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model.add(Activation('relu')) |
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model.add(Flatten()) |
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model.add(Dense(output_dim=fc_hidden_nodes, |
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W_regularizer=l2(regularization_rate), init=weightinit)) # Fully connected layer |
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model.add(Activation('relu')) # Relu activation |
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model.add(Dense(output_dim=outputdim, init=weightinit)) |
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model.add(BatchNormalization()) |
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model.add(Activation("softmax")) # Final classification layer |
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model.compile(loss='categorical_crossentropy', |
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optimizer=Adam(lr=learning_rate), |
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metrics=['accuracy']) |
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return model |
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def generate_CNN_hyperparameter_set(min_layers=1, max_layers=10, |
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min_filters=10, max_filters=100, |
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min_fc_nodes=10, max_fc_nodes=100, |
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low_lr=1, high_lr=4, low_reg=1, high_reg=4): |
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""" Generate a hyperparameter set that define a CNN model. |
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Parameters |
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---------- |
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min_layers : int |
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minimum of Conv layers |
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max_layers : int |
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maximum of Conv layers |
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min_filters : int |
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minimum number of filters per Conv layer |
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max_filters : int |
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maximum number of filters per Conv layer |
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min_fc_nodes : int |
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minimum number of hidden nodes per Dense layer |
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max_fc_nodes : int |
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maximum number of hidden nodes per Dense layer |
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low_lr : float |
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minimum of log range for learning rate: learning rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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238
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high_lr : float |
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maximum of log range for learning rate: learning rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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240
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low_reg : float |
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minimum of log range for regularization rate: regularization rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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242
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high_reg : float |
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maximum of log range for regularization rate: regularization rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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244
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245
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Returns |
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---------- |
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247
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hyperparameters : dict |
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248
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parameters for a CNN model |
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""" |
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hyperparameters = generate_base_hyper_parameter_set( |
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low_lr, high_lr, low_reg, high_reg) |
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number_of_layers = np.random.randint(min_layers, max_layers + 1) |
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hyperparameters['filters'] = np.random.randint( |
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min_filters, max_filters + 1, number_of_layers) |
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hyperparameters['fc_hidden_nodes'] = np.random.randint( |
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256
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min_fc_nodes, max_fc_nodes + 1) |
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return hyperparameters |
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260
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def generate_DeepConvLSTM_hyperparameter_set( |
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261
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min_conv_layers=1, max_conv_layers=10, |
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min_conv_filters=10, max_conv_filters=100, |
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min_lstm_layers=1, max_lstm_layers=5, |
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min_lstm_dims=10, max_lstm_dims=100, |
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low_lr=1, high_lr=4, low_reg=1, high_reg=4): |
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""" Generate a hyperparameter set that defines a DeepConvLSTM model. |
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267
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268
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Parameters |
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269
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---------- |
|
270
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min_conv_layers : int |
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271
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minimum number of Conv layers in DeepConvLSTM model |
|
272
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max_conv_layers : int |
|
273
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maximum number of Conv layers in DeepConvLSTM model |
|
274
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min_conv_filters : int |
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275
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minimum number of filters per Conv layer in DeepConvLSTM model |
|
276
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max_conv_filters : int |
|
277
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maximum number of filters per Conv layer in DeepConvLSTM model |
|
278
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min_lstm_layers : int |
|
279
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minimum number of Conv layers in DeepConvLSTM model |
|
280
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max_lstm_layers : int |
|
281
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maximum number of Conv layers in DeepConvLSTM model |
|
282
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min_lstm_dims : int |
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283
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minimum number of hidden nodes per LSTM layer in DeepConvLSTM model |
|
284
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max_lstm_dims : int |
|
285
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maximum number of hidden nodes per LSTM layer in DeepConvLSTM model |
|
286
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low_lr : float |
|
287
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minimum of log range for learning rate: learning rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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|
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|
|
288
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high_lr : float |
|
289
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maximum of log range for learning rate: learning rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
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|
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|
|
290
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low_reg : float |
|
291
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minimum of log range for regularization rate: regularization rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
|
|
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|
|
|
292
|
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high_reg : float |
|
293
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maximum of log range for regularization rate: regularization rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
|
|
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|
|
|
294
|
|
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|
|
295
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Returns |
|
296
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---------- |
|
297
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hyperparameters: dict |
|
298
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hyperparameters for a DeepConvLSTM model |
|
299
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""" |
|
300
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1 |
|
hyperparameters = generate_base_hyper_parameter_set( |
|
301
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low_lr, high_lr, low_reg, high_reg) |
|
302
|
1 |
|
number_of_conv_layers = np.random.randint( |
|
303
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|
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min_conv_layers, max_conv_layers + 1) |
|
304
|
1 |
|
hyperparameters['filters'] = np.random.randint( |
|
305
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|
|
min_conv_filters, max_conv_filters + 1, number_of_conv_layers) |
|
306
|
1 |
|
number_of_lstm_layers = np.random.randint( |
|
307
|
|
|
min_lstm_layers, max_lstm_layers + 1) |
|
308
|
1 |
|
hyperparameters['lstm_dims'] = np.random.randint( |
|
309
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min_lstm_dims, max_lstm_dims + 1, number_of_lstm_layers) |
|
310
|
1 |
|
return hyperparameters |
|
311
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|
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|
|
312
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|
|
313
|
1 |
|
def generate_base_hyper_parameter_set( |
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|
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|
|
314
|
|
|
low_lr=1, |
|
315
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|
|
high_lr=4, |
|
316
|
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|
low_reg=1, |
|
317
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|
|
high_reg=4): |
|
318
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|
""" Generate a base set of hyperparameters that are necessary for any model, but sufficient for none. |
|
|
|
|
|
|
319
|
|
|
|
|
320
|
|
|
Parameters |
|
321
|
|
|
---------- |
|
322
|
|
|
low_lr : float |
|
323
|
|
|
minimum of log range for learning rate: learning rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
|
|
|
|
|
|
324
|
|
|
high_lr : float |
|
325
|
|
|
maximum of log range for learning rate: learning rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
|
|
|
|
|
|
326
|
|
|
low_reg : float |
|
327
|
|
|
minimum of log range for regularization rate: regularization rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
|
|
|
|
|
|
328
|
|
|
high_reg : float |
|
329
|
|
|
maximum of log range for regularization rate: regularization rate is sampled between `10**(-low_reg)` and `10**(-high_reg)` |
|
|
|
|
|
|
330
|
|
|
|
|
331
|
|
|
Returns |
|
332
|
|
|
------- |
|
333
|
|
|
hyperparameters : dict |
|
334
|
|
|
basis hyperpameters |
|
335
|
|
|
""" |
|
336
|
1 |
|
hyperparameters = {} |
|
337
|
1 |
|
hyperparameters['learning_rate'] = get_learning_rate(low_lr, high_lr) |
|
338
|
1 |
|
hyperparameters['regularization_rate'] = get_regularization( |
|
339
|
|
|
low_reg, high_reg) |
|
340
|
1 |
|
return hyperparameters |
|
341
|
|
|
|
|
342
|
|
|
|
|
343
|
1 |
|
def get_learning_rate(low=1, high=4): |
|
344
|
|
|
""" Return random learning rate 10^-n where n is sampled uniformly between low and high bounds. |
|
|
|
|
|
|
345
|
|
|
|
|
346
|
|
|
Parameters |
|
347
|
|
|
---------- |
|
348
|
|
|
low : float |
|
349
|
|
|
low bound |
|
350
|
|
|
high : float |
|
351
|
|
|
high bound |
|
352
|
|
|
""" |
|
353
|
1 |
|
result = 10 ** (-np.random.uniform(low, high)) |
|
354
|
1 |
|
return result |
|
355
|
|
|
|
|
356
|
|
|
|
|
357
|
1 |
|
def get_regularization(low=1, high=4): |
|
358
|
|
|
""" Return random regularization rate 10^-n where n is sampled uniformly between low and high bounds. |
|
|
|
|
|
|
359
|
|
|
|
|
360
|
|
|
Parameters |
|
361
|
|
|
---------- |
|
362
|
|
|
low : float |
|
363
|
|
|
low bound |
|
364
|
|
|
high : float |
|
365
|
|
|
high bound |
|
366
|
|
|
""" |
|
367
|
|
|
return 10 ** (-np.random.uniform(low, high)) |
|
368
|
|
|
|
NLeSC /
mcfly
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