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model_search.py
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model_search.py
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import os
import pickle
import datetime
import logging as lgg
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from preprocessing_search import generate_train_test
from util import generate_modelling_data, get_train_validation_data
from tensorflow import keras
import kerastuner as kt
from kerastuner.tuners import RandomSearch
from tensorflow.keras.callbacks import EarlyStopping, TensorBoard, Callback
from kerastuner.tuners import Hyperband
BATCH_SIZE = 100000
SEED = 42
log_dir = "./logs/" + datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
early_stop_callback = EarlyStopping(
monitor="val_loss",
min_delta=1e-3,
patience=10,
verbose=0
)
tensorboard_callback = TensorBoard(
log_dir=log_dir,
histogram_freq=1,
embeddings_freq=1,
write_graph=True,
update_freq="batch"
)
def shallow_nn_model_builder(hp):
model = keras.Sequential()
model.add(keras.layers.Input(shape=(88,)))
hp_units = hp.Int("units", min_value=256, max_value=1792, step=256)
model.add(keras.layers.Dense(units=hp_units, activation="elu"))
model.add(keras.layers.Dense(50, activation="softmax"))
hp_learning_rate = hp.Choice("learning_rate", values=[0.05, 0.01, 0.0005, 0.0001])
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"]
)
return model
def deep_dense_nn_model_builder(hp):
model = keras.Sequential()
model.add(keras.layers.Input(shape=(88,)))
for layers in range(hp.Int("num_layers", 2, 8)):
model.add(
keras.layers.Dense(units=hp.Int(
f"units_{layers}",
min_value=256,
max_value=2048,
step=256
),
activation="elu")
)
model.add(keras.layers.Dense(50, activation="softmax"))
hp_learning_rate = hp.Choice("learning_rate", values=[0.0001, 0.0003, 0.0005])
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"]
)
return model
def long_thin_nn_model_builder(hp):
model = keras.Sequential()
model.add(keras.layers.Input(shape=(88,)))
for layers in range(hp.Int("num_layers", 5, 10)):
model.add(
keras.layers.Dense(units=hp.Int(
f"units_{layers}",
min_value=256,
max_value=768,
step=256
),
activation="elu")
)
model.add(keras.layers.Dense(50, activation="softmax"))
hp_learning_rate = hp.Choice("learning_rate", values=[0.0001, 0.0005, 0.0009])
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"]
)
return model
def long_thin_nn_model_round_two(hp):
model = keras.Sequential()
model.add(keras.layers.Input(shape=(88,)))
model.add(keras.layers.Dense(256, activation="relu"))
model.add(keras.layers.Dense(512, activation="relu"))
model.add(keras.layers.Dense(hp.Int(
"units_2",
min_value=448,
max_value=576,
step=64
), activation="relu"))
model.add(keras.layers.Dense(768, activation="relu"))
model.add(keras.layers.Dense(hp.Int(
"units_4",
min_value=448,
max_value=576,
step=64
), activation="relu"))
model.add(keras.layers.Dense(50, activation="softmax"))
hp_learning_rate = hp.Choice("learning_rate", values=[0.0009, 0.001, 0.0015, 0.002])
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"]
)
return model
def long_thin_nn_model_round_three(hp):
model = keras.Sequential()
model.add(keras.layers.Input(shape=(88,)))
model.add(keras.layers.Dense(256, activation="relu"))
model.add(keras.layers.Dense(512, activation="relu"))
model.add(keras.layers.Dense(hp.Int(
"units_2",
min_value=384,
max_value=640,
step=64
), activation="relu")) # still uncertaintly around this layer, widening search
model.add(keras.layers.Dense(768, activation="relu"))
model.add(keras.layers.Dense(512, activation="relu"))
model.add(keras.layers.Dense(50, activation="softmax"))
hp_learning_rate = hp.Choice("learning_rate", values=[0.0015, 0.002, 0.0025, 0.003])
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"]
)
return model
def run_search(model_builder, directory, project_name):
os.makedirs(directory, exist_ok=True)
tuner = RandomSearch(
model_builder,
objective="val_accuracy",
max_trials=16,
executions_per_trial=1,
seed=SEED,
directory=directory,
project_name=project_name
)
X_train, X_validation, y_train, y_validation = get_train_validation_data()
tuner.search(
X_train,
y_train,
verbose=1,
epochs=50,
batch_size=BATCH_SIZE,
validation_data=(X_validation, y_validation),
callbacks=[early_stop_callback, tensorboard_callback],
use_multiprocessing=True
)
tuner.search_space_summary()
tuner.results_summary()
if __name__ == "__main__":
run_search(
long_thin_nn_model_round_three,
"long_thin_nn_model_round_three",
"protocol"
)