Source code for nlp_architect.models.np_semantic_segmentation

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# Copyright 2017-2018 Intel Corporation
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import tensorflow as tf


# taken from keras previous versions: https://github.com/keras-team/keras/issues/5400
[docs]def precision_score(y_true, y_pred): """Precision metric. Only computes a batch-wise average of precision. Computes the precision, a metric for multi-label classification of how many selected items are relevant. """ K = tf.keras.backend true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return precision
[docs]def recall_score(y_true, y_pred): """Recall metric. Only computes a batch-wise average of recall. Computes the recall, a metric for multi-label classification of how many relevant items are selected. """ K = tf.keras.backend true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall
[docs]def f1(y_true, y_pred): """ Args: y_true: y_pred: Returns: """ K = tf.keras.backend precision = precision_score(y_true, y_pred) recall = recall_score(y_true, y_pred) return 2 * ((precision * recall) / (precision + recall + K.epsilon()))
[docs]class NpSemanticSegClassifier: """ NP Semantic Segmentation classifier model (based on tf.Keras framework). Args: num_epochs(int): number of epochs to train the model **callback_args (dict): callback args keyword arguments to init a Callback for the model loss: the model's cost function. Default is 'tf.keras.losses.binary_crossentropy' loss optimizer (:obj:`tf.keras.optimizers`): the model's optimizer. Default is 'adam' """ def __init__(self, num_epochs, callback_args, loss='binary_crossentropy', optimizer='adam', batch_size=128, ): """ Args: num_epochs(int): number of epochs to train the model callback_args (dict): callback args keyword arguments to init Callback for the model loss: the model's loss function. Default is 'tf.keras.losses.binary_crossentropy' loss optimizer (:obj:`tf.keras.optimizers`): the model's optimizer. Default is `adam` batch_size (int): batch size """ self.model = None self.loss = loss self.optimizer = optimizer self.epochs = num_epochs self.callback_args = callback_args self.batch_size = batch_size
[docs] def build(self, input_dim): """ Build the model's layers Args: input_dim (int): the first layer's input_dim """ first_layer_dens = 64 second_layer_dens = 64 output_layer_dens = 1 model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(first_layer_dens, activation='relu', input_dim=input_dim)) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(second_layer_dens, activation='relu')) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(output_layer_dens, activation='sigmoid')) metrics = ['binary_accuracy', precision_score, recall_score, f1] # Compile model model.compile(loss=self.loss, optimizer=self.optimizer, metrics=metrics) self.model = model
[docs] def fit(self, train_set): """ Train and fit the model on the datasets Args: train_set (:obj:`numpy.ndarray`): The train set args: callback_args and epochs from ArgParser input """ self.model.fit(train_set['X'], train_set['y'], epochs=self.epochs, batch_size=self.batch_size, verbose=2)
[docs] def save(self, model_path): """ Save the model's prm file in model_path location Args: model_path(str): local path for saving the model """ # serialize model to JSON model_json = self.model.to_json() with open(model_path[:-2] + 'json', 'w') as json_file: json_file.write(model_json) # serialize weights to HDF5 self.model.save_weights(model_path) print("Saved model to disk")
[docs] def load(self, model_path): """ Load pre-trained model's .h5 file to NpSemanticSegClassifier object Args: model_path(str): local path for loading the model """ # load json and create model with open(model_path[:-2] + 'json', 'r') as json_file: loaded_model_json = json_file.read() loaded_model = tf.keras.models.model_from_json(loaded_model_json) # load weights into new model loaded_model.load_weights(model_path) print("Loaded model from disk") self.model = loaded_model
[docs] def eval(self, test_set): """ Evaluate the model's test_set on error_rate, test_accuracy_rate and precision_recall_rate Args: test_set (:obj:`numpy.ndarray`): The test set Returns: tuple(float): loss, binary_accuracy, precision, recall and f1 measures """ return self.model.evaluate(test_set['X'], test_set['y'], batch_size=128, verbose=2)
[docs] def get_outputs(self, test_set): """ Classify the dataset on the model Args: test_set (:obj:`numpy.ndarray`): The test set Returns: list(:obj:`numpy.ndarray`): model's predictions """ return self.model.predict(test_set)