def test_resnet3d_101(resnet3d_test):
"""Test 101."""
K.set_image_data_format('channels_last')
model = Resnet3DBuilder.build_resnet_101((224, 224, 224, 1), 2)
resnet3d_test(model)
K.set_image_data_format('channels_first')
model = Resnet3DBuilder.build_resnet_101((1, 512, 512, 256), 2)
resnet3d_test(model)
batch_x.append(frames)
batch_y.append(labels)
batch_x = np.array(batch_x)
batch_y = np.array(batch_y)
batch_x = batch_x.reshape(-1, 30, 64, 96, 3)
yield batch_x, batch_y
train_generator = generator('train', batch_size=64)
validation_generator = generator('val', batch_size=64)
model = Resnet3DBuilder.build_resnet_101((30, 64, 96, 3), 27)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
callbacks = [
ModelCheckpoint('./resnet3d_50_dropout_3c_64x96_b32.h5',
monitor='val_accuracy',
verbose=1,
save_best_only=True),
EarlyStopping(patience=5)
]
history = model.fit(train_generator,
validation_data=validation_generator,
steps_per_epoch=119_000 // 64,
validation_steps=15_000 // 64,
callbacks=callbacks,
