from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import unittest
from unittest import skip
import sys
import os
import numpy as np
np.random.seed(1234)
from deeplift.conversion import kerasapi_conversion as kc
import deeplift.layers as layers
from deeplift.layers import NonlinearMxtsMode
from deeplift.util import compile_func
import tensorflow as tf
import keras
from keras import models
from keras import backend as K
class TestConvolutionalModel(unittest.TestCase):
def setUp(self):
self.inp = (np.random.randn(10*10*51*51)
.reshape(10,10,51,51)).transpose(0,2,3,1)
self.keras_model = keras.models.Sequential()
conv_layer1 = keras.layers.convolutional.Convolution2D(
nb_filter=20, nb_row=4, nb_col=4, subsample=(2,2),
activation="relu",
padding='same',
input_shape=(51,51,10))
self.keras_model.add(conv_layer1)
conv_layer2 = keras.layers.convolutional.Convolution2D(
nb_filter=10, nb_row=4, nb_col=4, subsample=(2,2),
activation="relu",
padding='same')
self.keras_model.add(conv_layer2)
self.keras_model.add(keras.layers.pooling.MaxPooling2D(
pool_size=(4,4), strides=(2,2),
padding='same'))
self.keras_model.add(keras.layers.pooling.AveragePooling2D(
pool_size=(4,4), strides=(2,2),
padding='same'))
self.keras_model.add(keras.layers.Flatten())
self.keras_model.add(keras.layers.Dense(output_dim=1))
self.keras_model.add(keras.layers.core.Activation("sigmoid"))
self.keras_model.compile(loss="mse", optimizer="sgd")
self.keras_output_fprop_func = compile_func(
[self.keras_model.layers[0].input,
K.learning_phase()],
self.keras_model.layers[-1].output)
grad = tf.gradients(tf.reduce_sum(
self.keras_model.layers[-2].output[:,0]),
[self.keras_model.layers[0].input])[0]
self.grad_func = compile_func(
[self.keras_model.layers[0].input,
K.learning_phase()], grad)
self.saved_file_path = "conv2model_samepadding.h5"
if (os.path.isfile(self.saved_file_path)):
os.remove(self.saved_file_path)
self.keras_model.save(self.saved_file_path)
def test_convert_conv2d_model_forward_prop(self):
deeplift_model =\
kc.convert_model_from_saved_files(
self.saved_file_path,
nonlinear_mxts_mode=NonlinearMxtsMode.Rescale)
deeplift_fprop_func = compile_func(
[deeplift_model.get_layers()[0].get_activation_vars()],
deeplift_model.get_layers()[-1].get_activation_vars())
np.testing.assert_almost_equal(
deeplift_fprop_func(self.inp),
self.keras_output_fprop_func([self.inp, 0]),
decimal=6)
def test_convert_conv2d_model_compute_scores(self):
deeplift_model =\
kc.convert_model_from_saved_files(
self.saved_file_path,
nonlinear_mxts_mode=NonlinearMxtsMode.Rescale)
deeplift_contribs_func = deeplift_model.\
get_target_contribs_func(
find_scores_layer_idx=0,
target_layer_idx=-2)
np.testing.assert_almost_equal(
deeplift_contribs_func(task_idx=0,
input_data_list=[self.inp],
batch_size=10,
progress_update=None),
#when biases are 0 and ref is 0, deeplift is the same as grad*inp
self.grad_func([self.inp, 0])*self.inp, decimal=6)
def tearDown(self):
if (os.path.isfile(self.saved_file_path)):
os.remove(self.saved_file_path)
