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''' |
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Summary: |
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Function generate_models from modelgen.py generates and compiles models |
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Function train_models_on_samples trains those models |
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Function plotTrainingProcess plots the training process |
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Function find_best_architecture is wrapper function that combines |
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these steps |
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Example function calls in 'EvaluateDifferentModels.ipynb' |
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''' |
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import numpy as np |
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from matplotlib import pyplot as plt |
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from . import modelgen |
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from sklearn import neighbors, metrics |
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import warnings |
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def train_models_on_samples(X_train, y_train, X_val, y_val, models, |
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nr_epochs=5, subsize_set=100, verbose=True): |
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''' |
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Given a list of compiled models, this function trains |
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them all on a subset of the train data. If the given size of the subset is |
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smaller then the size of the data, the complete data set is used. |
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Parameters |
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---------- |
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X_train : numpy array of shape (num_samples, num_timesteps, num_channels) |
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The input dataset for training |
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y_train : numpy array of shape (num_samples, num_classes) |
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The output classes for the train data, in binary format |
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X_val : numpy array of shape (num_samples_val, num_timesteps, num_channels) |
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The input dataset for validation |
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y_val : numpy array of shape (num_samples_val, num_classes) |
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The output classes for the validation data, in binary format |
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models : list of model, params, modeltypes |
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List of keras models to train |
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nr_epochs : int, optional |
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nr of epochs to use for training one model |
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subsize_set : int, optional |
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number of samples to use from the training set for training these models |
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verbose : bool, optional |
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flag for displaying verbose output |
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Returns |
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---------- |
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histories : list of Keras History objects |
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train histories for all models |
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val_accuracies : list of floats |
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validation accuraracies of the models |
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val_losses : list of floats |
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validation losses of the models |
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''' |
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# if subset_size is smaller then X_train, this will work fine |
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X_train_sub = X_train[:subsize_set, :, :] |
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y_train_sub = y_train[:subsize_set, :] |
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histories = [] |
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val_accuracies = [] |
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val_losses = [] |
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for model, params, model_types in models: |
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history = model.fit(X_train_sub, y_train_sub, |
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nb_epoch=nr_epochs, batch_size=20, # see comment on subsize_set |
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validation_data=(X_val, y_val), |
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verbose=verbose) |
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histories.append(history) |
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val_accuracies.append(history.history['val_acc'][-1]) |
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val_losses.append(history.history['val_loss'][-1]) |
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return histories, val_accuracies, val_losses |
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def plotTrainingProcess(history, name='Model', ax=None): |
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''' |
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This function plots the loss and accuracy on the train and validation set, |
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for each epoch in the history of one model. |
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Parameters |
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---------- |
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history : keras History object for one model |
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The history object of the training process corresponding to one model |
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Returns |
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---------- |
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''' |
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if ax is None: |
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fig, ax = plt.subplots() |
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ax2 = ax.twinx() |
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LN = len(history.history['val_loss']) |
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val_loss, = ax.plot(range(LN), history.history['val_loss'], 'g--', |
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label='validation loss') |
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train_loss, = ax.plot(range(LN), history.history['loss'], 'g-', |
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label='train loss') |
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val_acc, = ax2.plot(range(LN), history.history['val_acc'], 'b--', |
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label='validation accuracy') |
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train_acc, = ax2.plot(range(LN), history.history['acc'], 'b-', |
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label='train accuracy') |
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ax.set_xlabel('epoch') |
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ax.set_ylabel('loss', color='g') |
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ax2.set_ylabel('accuracy', color='b') |
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plt.legend(handles=[val_loss, train_loss, val_acc, train_acc], |
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loc=2, bbox_to_anchor=(1.1, 1)) |
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plt.title(name) |
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def find_best_architecture(X_train, y_train, X_val, y_val, verbose=True, |
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number_of_models=5, nr_epochs=5, **kwargs): |
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''' |
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Tries out a number of models on a subsample of the data, |
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and outputs the best found architecture and hyperparameters. |
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Parameters |
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---------- |
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X_train : numpy array of shape (num_samples, num_timesteps, num_channels) |
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The input dataset for training |
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y_train : numpy array of shape (num_samples, num_classes) |
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The output classes for the train data, in binary format |
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X_val : numpy array of shape (num_samples_val, num_timesteps, num_channels) |
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The input dataset for validation |
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y_val : numpy array of shape (num_samples_val, num_classes) |
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The output classes for the validation data, in binary format |
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verbose : bool, optional |
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flag for displaying verbose output |
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**kwargs: key-value parameters |
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parameters for generating the models |
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Returns |
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---------- |
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best_model : Keras model |
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Best performing model, already trained on a small sample data set. |
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best_params : dict |
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Dictionary containing the hyperparameters for the best model |
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best_model_type : str |
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Type of the best model |
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knn_acc : float |
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accuaracy for kNN prediction on validation set |
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''' |
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models = modelgen.generate_models(X_train.shape, y_train.shape[1], |
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number_of_models=number_of_models, |
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**kwargs) |
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subsize_set = 100 |
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histories, val_accuracies, val_losses = train_models_on_samples(X_train, |
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y_train, |
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X_val, |
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y_val, |
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models, |
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nr_epochs, |
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subsize_set=subsize_set, |
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verbose=verbose) |
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best_model_index = np.argmax(val_accuracies) |
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best_model, best_params, best_model_type = models[best_model_index] |
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knn_acc = kNN_accuracy(X_train[:subsize_set, :, :], y_train[:subsize_set, :], X_val, y_val) |
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if verbose: |
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for i in range(len(models)): #<= now one plot per model, ultimately we |
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# may want all models in one plot to allow for direct comparison |
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name = str(models[i][1]) |
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plotTrainingProcess(histories[i], name) |
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print('Best model: model ', best_model_index) |
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print('Model type: ', best_model_type) |
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print('Hyperparameters: ', best_params) |
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print('Accuracy on validation set: ', val_accuracies[best_model_index]) |
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print('Accuracy of kNN on validation set', knn_acc) |
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if val_accuracies[best_model_index] < knn_acc: |
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warnings.warn('Best model not beter than kNN: ' + |
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str(val_accuracies[best_model_index]) + ' vs ' + |
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str(knn_acc) |
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) |
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return best_model, best_params, best_model_type, knn_acc |
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def kNN_accuracy(X_train, y_train, X_val, y_val, k=1): |
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num_samples, num_timesteps, num_channels = X_train.shape |
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clf = neighbors.KNeighborsClassifier(k) |
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clf.fit(X_train.reshape(num_samples, num_timesteps*num_channels), y_train) |
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num_samples, num_timesteps, num_channels = X_val.shape |
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val_predict = clf.predict(X_val.reshape(num_samples, num_timesteps*num_channels)) |
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return metrics.accuracy_score(val_predict, y_val) |
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