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model_structure.py
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model_structure.py
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import tensorflow as tf
import os
def create_model():
"""Specifies the structure of a seq2point model using Keras' functional API.
Returns:
model (tensorflow.keras.Model): The uncompiled seq2point model.
"""
input_layer = tf.keras.layers.Input(shape=(599,))
reshape_layer = tf.keras.layers.Reshape((1, 599, 1))(input_layer)
conv_layer_1 = tf.keras.layers.Convolution2D(filters=30, kernel_size=(10, 1), strides=(1, 1), padding="same", activation="relu")(reshape_layer)
conv_layer_2 = tf.keras.layers.Convolution2D(filters=30, kernel_size=(8, 1), strides=(1, 1), padding="same", activation="relu")(conv_layer_1)
conv_layer_3 = tf.keras.layers.Convolution2D(filters=40, kernel_size=(6, 1), strides=(1, 1), padding="same", activation="relu")(conv_layer_2)
conv_layer_4 = tf.keras.layers.Convolution2D(filters=50, kernel_size=(5, 1), strides=(1, 1), padding="same", activation="relu")(conv_layer_3)
conv_layer_5 = tf.keras.layers.Convolution2D(filters=50, kernel_size=(5, 1), strides=(1, 1), padding="same", activation="relu")(conv_layer_4)
flatten_layer = tf.keras.layers.Flatten()(conv_layer_5)
label_layer = tf.keras.layers.Dense(1024, activation="relu")(flatten_layer)
output_layer = tf.keras.layers.Dense(1, activation="linear")(label_layer)
model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
return model
def create_dropout_model():
""" Specifies the structure of a seq2point with dropout model using Keras' functional API.
Returns:
model (tensorflow.keras.Model): The uncompiled seq2point model with dropout layers.
"""
input_layer = tf.keras.layers.Input(shape=(599,))
reshape_layer = tf.keras.layers.Reshape((1, 599, 1))(input_layer)
conv_layer_1 = tf.keras.layers.Convolution2D(filters=30, kernel_size=(10, 1), strides=(1, 1), padding="same", activation="relu")(reshape_layer)
dropout_layer_1 = tf.keras.layers.Dropout(0.5)(conv_layer_1)
conv_layer_2 = tf.keras.layers.Convolution2D(filters=30, kernel_size=(8, 1), strides=(1, 1), padding="same", activation="relu")(dropout_layer_1)
dropout_layer_2 = tf.keras.layers.Dropout(0.5)(conv_layer_2)
conv_layer_3 = tf.keras.layers.Convolution2D(filters=40, kernel_size=(6, 1), strides=(1, 1), padding="same", activation="relu")(dropout_layer_2)
dropout_layer_3 = tf.keras.layers.Dropout(0.5)(conv_layer_3)
conv_layer_4 = tf.keras.layers.Convolution2D(filters=50, kernel_size=(5, 1), strides=(1, 1), padding="same", activation="relu")(dropout_layer_3)
dropout_layer_4 = tf.keras.layers.Dropout(0.5)(conv_layer_4)
conv_layer_5 = tf.keras.layers.Convolution2D(filters=50, kernel_size=(5, 1), strides=(1, 1), padding="same", activation="relu")(dropout_layer_4)
dropout_layer_5 = tf.keras.layers.Dropout(0.5)(conv_layer_5)
flatten_layer = tf.keras.layers.Flatten()(dropout_layer_5)
label_layer = tf.keras.layers.Dense(1024, activation="relu")(flatten_layer)
dropout_layer_6 = tf.keras.layers.Dropout(0.5)(label_layer)
output_layer = tf.keras.layers.Dense(1, activation="linear")(dropout_layer_6)
model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
return model
def create_reduced_model():
""" Create a seq2point model with 10 fewer filters per convolutional layer and 2^9 instead
of 2^10 hidden layer neurons.
Returns:
model (tensorflow.keras.Model): The uncompiled seq2point model with fewer filters and hidden neurons.
"""
input_layer = tf.keras.layers.Input(shape=(599,))
reshape_layer = tf.keras.layers.Reshape((1, 599, 1))(input_layer)
conv_layer_1 = tf.keras.layers.Convolution2D(filters=20, kernel_size=(8, 1), strides=(1, 1), padding="same", activation="relu")(reshape_layer)
conv_layer_2 = tf.keras.layers.Convolution2D(filters=20, kernel_size=(6, 1), strides=(1, 1), padding="same", activation="relu")(conv_layer_1)
conv_layer_3 = tf.keras.layers.Convolution2D(filters=30, kernel_size=(5, 1), strides=(1, 1), padding="same", activation="relu")(conv_layer_2)
conv_layer_4 = tf.keras.layers.Convolution2D(filters=40, kernel_size=(4, 1), strides=(1, 1), padding="same", activation="relu")(conv_layer_3)
conv_layer_5 = tf.keras.layers.Convolution2D(filters=40, kernel_size=(4, 1), strides=(1, 1), padding="same", activation="relu")(conv_layer_4)
flatten_layer = tf.keras.layers.Flatten()(conv_layer_5)
label_layer = tf.keras.layers.Dense(512, activation="relu")(flatten_layer)
output_layer = tf.keras.layers.Dense(1, activation="linear")(label_layer)
model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
return model
def create_reduced_dropout_model():
""" Applies dropout to the reduced seq2point architecture.
Returns:
model (tensorflow.keras.Model): The uncompiled seq2point model with dropout layers,
fewer filters, and fewer hidden neurons.
"""
input_layer = tf.keras.layers.Input(shape=(599,))
reshape_layer = tf.keras.layers.Reshape((1, 599, 1))(input_layer)
conv_layer_1 = tf.keras.layers.Convolution2D(filters=20, kernel_size=(8, 1), strides=(1, 1), padding="same", activation="relu")(reshape_layer)
dropout_layer_1 = tf.keras.layers.Dropout(0.5)(conv_layer_1)
conv_layer_2 = tf.keras.layers.Convolution2D(filters=20, kernel_size=(6, 1), strides=(1, 1), padding="same", activation="relu")(dropout_layer_1)
dropout_layer_2 = tf.keras.layers.Dropout(0.5)(conv_layer_2)
conv_layer_3 = tf.keras.layers.Convolution2D(filters=30, kernel_size=(5, 1), strides=(1, 1), padding="same", activation="relu")(dropout_layer_2)
dropout_layer_3 = tf.keras.layers.Dropout(0.5)(conv_layer_3)
conv_layer_4 = tf.keras.layers.Convolution2D(filters=40, kernel_size=(4, 1), strides=(1, 1), padding="same", activation="relu")(dropout_layer_3)
dropout_layer_4 = tf.keras.layers.Dropout(0.5)(conv_layer_4)
conv_layer_5 = tf.keras.layers.Convolution2D(filters=40, kernel_size=(4, 1), strides=(1, 1), padding="same", activation="relu")(dropout_layer_4)
dropout_layer_5 = tf.keras.layers.Dropout(0.5)(conv_layer_5)
flatten_layer = tf.keras.layers.Flatten()(dropout_layer_5)
label_layer = tf.keras.layers.Dense(512, activation="relu")(flatten_layer)
dropout_layer_6 = tf.keras.layers.Dropout(0.5)(label_layer)
output_layer = tf.keras.layers.Dense(1, activation="linear")(dropout_layer_6)
model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
return model
def save_model(model, network_type, algorithm, appliance):
""" Saves a model to a specified location. Models are named using a combination of their
target appliance, architecture, and pruning algorithm.
Parameters:
model (tensorflow.keras.Model): The Keras model to save.
network_type (string): The architecture of the model ('', 'reduced', 'dropout', or 'reduced_dropout').
algorithm (string): The pruning algorithm applied to the model.
appliance (string): The appliance the model was trained with.
"""
model_path = "./" + appliance + "/saved_models/" + appliance + "_" + algorithm + "_" + network_type + "_model.h5"
if not os.path.exists (model_path):
open((model_path), 'a').close()
model.save(model_path)
def load_model(model, network_type, algorithm, appliance):
""" Loads a model from a specified location.
Parameters:
model (tensorflow.keras.Model): The Keas model to which the loaded weights will be applied to.
network_type (string): The architecture of the model ('', 'reduced', 'dropout', or 'reduced_dropout').
algorithm (string): The pruning algorithm applied to the model.
appliance (string): The appliance the model was trained with.
"""
model_name = "./" + appliance + "/saved_models/" + appliance + "_" + algorithm + "_" + network_type + "_model.h5"
print("PATH NAME: ", model_name)
model = tf.keras.models.load_model(model_name)
num_of_weights = model.count_params()
print("Loaded model with ", str(num_of_weights), " weights")
return model