src/classifier.py
def train_generator(self, training_iterator, validation_iterator):
        """
        Fit the model to the generator training data using keras optimization
        builtins.

        For example, we may want to minimize cross-entropy as a performance
        measure. This is built-in to keras (I'm almost positive).

        Params
        ------
        training_iterator: DataIterator
            training set lazily loaded to memory
        validation_iterator: DataIterator
            validation set lazily loaded to memory

        Returns
        -------
        [N/A called for side-effects only]
        """

        self.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
        self.fit_generator(
            training_iterator, 
            validation_data=validation_iterator,
            steps_per_epoch=16,
            validation_steps=8,
        )

        Fit the model to the generator training data using keras optimization
        builtins.

        For example, we may want to minimize cross-entropy as a performance
        measure. This is built-in to keras (I'm almost positive).

        Params
        ------
        training_iterator: DataIterator
            training set lazily loaded to memory
        validation_iterator: DataIterator
            validation set lazily loaded to memory

        Returns
        -------
        [N/A called for side-effects only]
        
Fit the model to the generator training data using keras optimization
builtins.

For example, we may want to minimize cross-entropy as a performance
measure. This is built-in to keras (I'm almost positive).

Params

DataIterator
training set lazily loaded to memory
validation_iterator: DataIterator
validation set lazily loaded to memory

Returns

[N/A called for side-effects only]
Fit the model to the generator training data using keras optimization builtins . For example we may want to minimize cross - entropy as a performance measure . This is built - in to keras ( I ' m almost positive ) . Params DataIterator training set lazily loaded to memory validation_iterator : DataIterator validation set lazily loaded to memory Returns [ N / A called for side - effects only ]
def train_generator(self, training_iterator, validation_iterator):
        self.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
        self.fit_generator(
            training_iterator, 
            validation_data=validation_iterator,
            steps_per_epoch=16,
            validation_steps=8,
        )
def train_generator ( self , training_iterator , validation_iterator ) : self . compile ( optimizer = "adam" , loss = "sparse_categorical_crossentropy" , metrics = [ "accuracy" ] ) self . fit_generator ( training_iterator , validation_data = validation_iterator , steps_per_epoch = 16 , validation_steps = 8 , )
Fit the model to the generator training data using keras optimization
builtins.
Fit the model to the generator training data using keras optimization builtins .
"""
        Fit the model to the generator training data using keras optimization
        builtins.

        For example, we may want to minimize cross-entropy as a performance
        measure. This is built-in to keras (I'm almost positive).

        Params
        ------
        training_iterator: DataIterator
            training set lazily loaded to memory
        validation_iterator: DataIterator
            validation set lazily loaded to memory

        Returns
        -------
        [N/A called for side-effects only]
        """
self
training_iterator
validation_iterator