configuration.py

# Copyright 2016 Syed Ahmed. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""Matching Networks model and training configurations."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


class ModelConfig(object):
    """Wrapper class for model hyperparameters."""

    def __init__(self):
        """Sets the default model hyperparameters."""
        # File pattern of sharded TFRecord file containing SequenceExample protos.
        # Must be provided in training and val modes.
        self.input_file_pattern = None

        # Sound format ("wav").
        self.sound_format = "wav"


        # Batch size.
        self.batch_size_s = 1
        self.batch_size_b = 1
        self.num_classes = 5

        # File containing an Inception v3 checkpoint to initialize the variables
        # of the Inception model. Must be provided when starting training for the
        # first time.
        self.f_checkpoint_file = None
        self.g_checkpoint_file = None

        # Dimensions of model sound inputs
        self.num_mels = 128
        self.frames = 64
        self.lstm_processing_steps = 10
        # Scale used to initialize model variables.
        self.initializer_scale = 0.08

        # LSTM input and output dimensionality, respectively.
        self.embedding_size = 64
        self.num_lstm_units = 64

        # If < 1.0, the dropout keep probability applied to LSTM variables.
        self.lstm_dropout_keep_prob = 0.7


class TrainingConfig(object):
    """Wrapper class for training hyperparameters."""

    def __init__(self):
        """Sets the default training hyperparameters."""
        # Number of examples per epoch of training data.
        self.num_examples_per_epoch = 586363

        # Optimizer for training the model.
        self.optimizer = "SGD"

        # Learning rate for the initial phase of training.
        self.initial_learning_rate = 2.0
        self.learning_rate_decay_factor = 0.5
        self.num_epochs_per_decay = 8.0

        # Learning rate when fine tuning the Inception v3 parameters.
        self.train_inception_learning_rate = 0.0005

        # If not None, clip gradients to this value.
        self.clip_gradients = 5.0

        # How many model checkpoints to keep.
        self.max_checkpoints_to_keep = 5