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inception_time.py
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# resnet model InceptionTime
# Code modified from https://github.com/hfawaz/InceptionTime
#import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from tensorflow.keras.models import Model
from tensorflow.keras.models import load_model
from tensorflow.keras import optimizers
#import numpy as np
#import time
#from utils.utils import save_logs
#from utils.utils import calculate_metrics
#from utils.utils import save_test_duration
class InceptionTime:
def __init__(self,
output_directory,
input_shape,
nb_classes,
verbose=False,
build=True,
batch_size=64,
nb_filters=32,
use_residual=True,
use_bottleneck=True,
depth=6,
kernel_size=41,
nb_epochs=1500):
"""
"""
self.output_directory = output_directory
self.nb_filters = nb_filters
self.use_residual = use_residual
self.use_bottleneck = use_bottleneck
self.depth = depth
self.kernel_size = kernel_size - 1
self.callbacks = None
self.batch_size = batch_size
self.bottleneck_size = 32
self.nb_epochs = nb_epochs
if build == True:
self.model = self.build_model(input_shape, nb_classes)
if (verbose == True):
self.model.summary()
self.verbose = verbose
self.model.save_weights(self.output_directory + 'model_init.hdf5')
def _inception_module(self, input_tensor, stride=1, activation='linear'):
if self.use_bottleneck and int(input_tensor.shape[-1]) > 1:
input_inception = layers.Conv1D(filters=self.bottleneck_size, kernel_size=1,
padding='same', activation=activation, use_bias=False)(input_tensor)
else:
input_inception = input_tensor
# kernel_size_s = [3, 5, 8, 11, 17]
kernel_size_s = [self.kernel_size // (2 ** i) for i in range(3)]
conv_list = []
for i in range(len(kernel_size_s)):
conv_list.append(layers.Conv1D(filters=self.nb_filters, kernel_size=kernel_size_s[i],
strides=stride, padding='same', activation=activation, use_bias=False)(
input_inception))
max_pool_1 = layers.MaxPool1D(pool_size=3, strides=stride, padding='same')(input_tensor)
conv_6 = layers.Conv1D(filters=self.nb_filters, kernel_size=1,
padding='same', activation=activation, use_bias=False)(max_pool_1)
conv_list.append(conv_6)
x = layers.Concatenate(axis=2)(conv_list)
x = layers.BatchNormalization()(x)
x = layers.Activation(activation='relu')(x)
return x
def _shortcut_layer(self, input_tensor, out_tensor):
shortcut_y = layers.Conv1D(filters=int(out_tensor.shape[-1]), kernel_size=1,
padding='same', use_bias=False)(input_tensor)
shortcut_y = layers.BatchNormalization()(shortcut_y)
x = layers.Add()([shortcut_y, out_tensor])
x = layers.Activation('relu')(x)
return x
def build_model(self, input_shape, nb_classes):
input_layer = layers.Input(input_shape)
x = input_layer
input_res = input_layer
for d in range(self.depth):
x = self._inception_module(x)
if self.use_residual and d % 3 == 2:
x = self._shortcut_layer(input_res, x)
input_res = x
gap_layer = layers.GlobalAveragePooling1D()(x)
output_layer = layers.Dense(nb_classes, activation='softmax')(gap_layer)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(),
metrics=['accuracy'])
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5, patience=50,
min_lr=0.0001)
file_path = self.output_directory + 'best_model.hdf5'
model_checkpoint = ModelCheckpoint(filepath=file_path, monitor='loss',
save_best_only=True)
self.callbacks = [reduce_lr, model_checkpoint]
return model
"""
def fit(self, x_train, y_train, x_val, y_val, y_true, plot_test_acc=False):
#if len(keras.backend.tensorflow_backend._get_available_gpus()) == 0:
# print('error no gpu')
# exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
if self.batch_size is None:
mini_batch_size = int(min(x_train.shape[0] / 10, 16))
else:
mini_batch_size = self.batch_size
start_time = time.time()
if plot_test_acc:
hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size, epochs=self.nb_epochs,
verbose=self.verbose, validation_data=(x_val, y_val), callbacks=self.callbacks)
else:
hist = self.model.fit(x_train, y_train, batch_size=mini_batch_size, epochs=self.nb_epochs,
verbose=self.verbose, callbacks=self.callbacks)
duration = time.time() - start_time
self.model.save(self.output_directory + 'last_model.hdf5')
y_pred = self.predict(x_val, y_true, x_train, y_train, y_val,
return_df_metrics=False)
# save predictions
np.save(self.output_directory + 'y_pred.npy', y_pred)
# convert the predicted from binary to integer
y_pred = np.argmax(y_pred, axis=1)
df_metrics = save_logs(self.output_directory, hist, y_pred, y_true, duration,
plot_test_acc=plot_test_acc)
#keras.backend.clear_session()
return df_metrics
def predict(self, x_test, y_true, x_train, y_train, y_test, return_df_metrics=True):
start_time = time.time()
model_path = self.output_directory + 'best_model.hdf5'
model = load_model(model_path)
y_pred = model.predict(x_test, batch_size=self.batch_size)
if return_df_metrics:
y_pred = np.argmax(y_pred, axis=1)
df_metrics = calculate_metrics(y_true, y_pred, 0.0)
return df_metrics
else:
test_duration = time.time() - start_time
save_test_duration(self.output_directory + 'test_duration.csv', test_duration)
return y_pred
"""