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EJ. Updated inception.py to work with UCR Archcive #35

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EJ. Updated inception.py to work with UCR Archcive #35

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32 changes: 13 additions & 19 deletions classifiers/inception.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
# resnet model
import keras
import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import time

Expand All @@ -10,7 +10,7 @@

class Classifier_INCEPTION:

def __init__(self, output_directory, input_shape, nb_classes, verbose=False, build=True, batch_size=64,
def __init__(self, output_directory, input_shape, nb_classes, verbose=False, build=True, batch_size=64, lr=0.001,
nb_filters=32, use_residual=True, use_bottleneck=True, depth=6, kernel_size=41, nb_epochs=1500):

self.output_directory = output_directory
Expand All @@ -24,17 +24,18 @@ def __init__(self, output_directory, input_shape, nb_classes, verbose=False, bui
self.batch_size = batch_size
self.bottleneck_size = 32
self.nb_epochs = nb_epochs
self.lr = lr
self.verbose = verbose

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:
if self.use_bottleneck and int(input_tensor.shape[-1]) > self.bottleneck_size:
input_inception = keras.layers.Conv1D(filters=self.bottleneck_size, kernel_size=1,
padding='same', activation=activation, use_bias=False)(input_tensor)
else:
Expand Down Expand Up @@ -65,7 +66,7 @@ def _inception_module(self, input_tensor, stride=1, activation='linear'):
def _shortcut_layer(self, input_tensor, out_tensor):
shortcut_y = keras.layers.Conv1D(filters=int(out_tensor.shape[-1]), kernel_size=1,
padding='same', use_bias=False)(input_tensor)
shortcut_y = keras.layers.normalization.BatchNormalization()(shortcut_y)
shortcut_y = keras.layers.BatchNormalization()(shortcut_y)

x = keras.layers.Add()([shortcut_y, out_tensor])
x = keras.layers.Activation('relu')(x)
Expand All @@ -91,7 +92,7 @@ def build_model(self, input_shape, nb_classes):

model = keras.models.Model(inputs=input_layer, outputs=output_layer)

model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(),
model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(self.lr),
metrics=['accuracy'])

reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=50,
Expand All @@ -106,8 +107,8 @@ def build_model(self, input_shape, nb_classes):

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:
def fit(self, x_train, y_train, x_val, y_val, y_true):
if not tf.test.is_gpu_available:
print('error no gpu')
exit()
# x_val and y_val are only used to monitor the test loss and NOT for training
Expand All @@ -119,14 +120,8 @@ def fit(self, x_train, y_train, x_val, y_val, y_true, plot_test_acc=False):

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)
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)

duration = time.time() - start_time

Expand All @@ -141,8 +136,7 @@ def fit(self, x_train, y_train, x_val, y_val, y_true, plot_test_acc=False):
# 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)
df_metrics = save_logs(self.output_directory, hist, y_pred, y_true, duration)

keras.backend.clear_session()

Expand Down