Expand source code
from __future__ import print_function
from __future__ import division
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.simplefilter(action='ignore', category=Warning)
import sys
import os
import GPUtil
import psutil
stderr = sys.stderr
sys.stderr = open(os.devnull, 'w')
import keras
sys.stderr = stderr
import tensorflow as tf
import networkx as nx
from matplotlib import pyplot as plt
if(tf.__version__.split(".")[0] == "2"):
import tensorflow.compat.v1.keras.backend as K
else:
from keras import backend as K
import keras.activations as kra
import keras.layers as krl
layer_names = ["convolution1d", "convolution2d", "convolution", "convolution3d", "transposed_convolution",
"transposed_convolution2d", "transposed_convolution3d", "max_pooling1d", "max_pooling2d",
"max_pooling", "max_pooling3d", "average_pooling1d", "average_pooling2d", "average_pooling",
"average_pooling3d", "global_max_pooling1d", "global_max_pooling2d", "global_max_pooling",
"global_max_pooling3d", "global_average_pooling1d", "global_average_pooling2d", "global_average_pooling",
"global_average_pooling3d", "flatten", "fully_connected", "dropout", "identity", "batch_normalization",
"relu", "elu", "leaky_relu", "prelu", "thresholded_relu", "softmax", "add", "concatenate",
"selu", "softplus", "softsign", "tanh", "sigmoid", "hard_sigmoid"]
names = ["conv1d_", "conv2d_", "conv_", "conv3d_", "tconv_",
"tconv2d_", "tconv3d_", "max-pool1d_", "max-pool2d_",
"max-pool_", "max-pool3d_", "avg-pool1d_", "avg-pool2d_", "avg-pool_",
"avg-pool3d_", "global-max-pool1d_", "global-max-pool2d_", "global-max-pool_",
"global-max-pool3d_", "global-avg-pool1d_", "global-avg-pool2d_", "global-avg-pool_",
"global-avg-pool3d_", "flatten_", "fc_", "dropout_", "identity_", "bn_",
"relu_", "elu_", "lrelu_", "prelu_", "trelu_", "softmax_", "add_", "concat_",
"selu_", "softplus_", "softsign_", "tanh_", "sigmoid_", "hard_sigmoid_"]