def build_model(self, n_features, n_classes):
"""Create the computational graph of the model.
:param n_features: Number of features.
:param n_classes: number of classes.
:return: self
"""
self._create_placeholders(n_features, n_classes)
self._create_layers(n_classes)
self.cost = self.loss.compile(self.mod_y, self.input_labels)
self.train_step = self.trainer.compile(self.cost)
self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
def build_model(self, n_features, n_classes):
"""Create the computational graph of the model.
:param n_features: Number of features.
:param n_classes: number of classes.
:return: self
"""
self._create_placeholders(n_features, n_classes)
self._create_layers(n_classes)
self.cost = self.loss.compile(self.mod_y, self.input_labels)
self.train_step = self.trainer.compile(self.cost)
self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
def build_model(self, n_features, n_classes):
"""Create the computational graph.
This graph is intented to be created for finetuning,
i.e. after unsupervised pretraining.
:param n_features: Number of features.
:param n_classes: number of classes.
:return: self
"""
self._create_placeholders(n_features, n_classes)
self._create_variables(n_features)
self.global_step = tf.Variable(0, dtype=tf.int32)
self.trainer = Trainer(
self.opt, global_step=self.global_step, decay_step=self.decay_step, decay_rate=self.decay_rate, learning_rate=self.learning_rate,
momentum=self.momentum)
self.next_train = self._create_encoding_layers()
self.mod_y, _, _ = Layers.linear(self.next_train, n_classes)
self.layer_nodes.append(self.mod_y)
self.cost = self.loss.compile(self.mod_y, self.input_labels)
self.train_step = self.trainer.compile(self.cost, self.global_step)
self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
self.precision = Evaluation.precision(self.mod_y, self.input_labels)
self.recall = Evaluation.recall(self.mod_y, self.input_labels)
def build_model(self, n_features, n_classes):
"""Create the computational graph.
:param n_features: number of features
:param n_classes: number of classes
:return: self
"""
self._create_placeholders(n_features, n_classes)
self._create_variables(n_features, n_classes)
self.mod_y = tf.nn.softmax(
tf.add(tf.matmul(self.input_data, self.W_), self.b_))
self.cost = self.loss.compile(self.mod_y, self.input_labels)
self.train_step = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.cost)
self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
def build_model(self, n_features, n_classes):
"""Create the computational graph.
:param n_features: number of features
:param n_classes: number of classes
:return: self
"""
self._create_placeholders(n_features, n_classes)
self._create_variables(n_features, n_classes)
self.mod_y = tf.nn.softmax(
tf.add(tf.matmul(self.input_data, self.W_), self.b_))
self.cost = self.loss.compile(self.mod_y, self.input_labels)
self.train_step = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.cost)
self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
def build_model(self, n_features, n_classes):
"""Create the computational graph.
This graph is intented to be created for finetuning,
i.e. after unsupervised pretraining.
:param n_features: Number of features.
:param n_classes: number of classes.
:return: self
"""
self._create_placeholders(n_features, n_classes)
self._create_variables(n_features)
next_train = self._create_encoding_layers()
self.mod_y, _, _ = Layers.linear(next_train, n_classes)
self.layer_nodes.append(self.mod_y)
self.cost = self.loss.compile(self.mod_y, self.input_labels)
self.train_step = self.trainer.compile(self.cost)
self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
def build_model(self, n_features, n_classes):
"""Create the computational graph.
This graph is intented to be created for finetuning,
i.e. after unsupervised pretraining.
:param n_features: Number of features.
:param n_classes: number of classes.
:return: self
"""
self._create_placeholders(n_features, n_classes)
self._create_variables(n_features)
next_train = self._create_encoding_layers()
self.mod_y, _, _ = Layers.linear(next_train, n_classes)
self.layer_nodes.append(self.mod_y)
self.cost = self.loss.compile(self.mod_y, self.input_labels)
self.train_step = self.trainer.compile(self.cost)
self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)