From fe6ec44f954ec6f1e052dce58c1d3fd3397c8100 Mon Sep 17 00:00:00 2001 From: Somshubra Majumdar Date: Mon, 13 Feb 2017 00:22:25 -0600 Subject: [PATCH] PEP8 fixes --- keras_contrib/applications/densenet.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/keras_contrib/applications/densenet.py b/keras_contrib/applications/densenet.py index 539dd0b..c53fba5 100644 --- a/keras_contrib/applications/densenet.py +++ b/keras_contrib/applications/densenet.py @@ -26,7 +26,6 @@ from keras.engine.topology import get_source_inputs from keras.applications.imagenet_utils import _obtain_input_shape import keras.backend as K - TH_WEIGHTS_PATH = 'https://github.com/titu1994/DenseNet/releases/download/v2.0/DenseNet-40-12-Theano-Backend-TH-dim-ordering.h5' TF_WEIGHTS_PATH = 'https://github.com/titu1994/DenseNet/releases/download/v2.0/DenseNet-40-12-Tensorflow-Backend-TF-dim-ordering.h5' TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/titu1994/DenseNet/releases/download/v2.0/DenseNet-40-12-Theano-Backend-TH-dim-ordering-no-top.h5.h5' @@ -107,8 +106,8 @@ def DenseNet(depth=40, nb_dense_block=3, growth_rate=12, nb_filter=16, img_input = input_tensor x = __create_dense_net(classes, img_input, include_top, depth, nb_dense_block, - growth_rate, nb_filter, bottleneck, reduction, - dropout_rate, weight_decay) + growth_rate, nb_filter, bottleneck, reduction, + dropout_rate, weight_decay) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. @@ -122,7 +121,7 @@ def DenseNet(depth=40, nb_dense_block=3, growth_rate=12, nb_filter=16, # load weights if weights == 'cifar10': if (depth == 40) and (nb_dense_block == 3) and (growth_rate == 12) and (nb_filter == 16) and \ - (bottleneck == False) and (reduction == 0.0) and (dropout_rate == 0.0) and (weight_decay == 1E-4): + (bottleneck is False) and (reduction == 0.0) and (dropout_rate == 0.0) and (weight_decay == 1E-4): # Default parameters match. Weights for this model exist: if K.image_dim_ordering() == 'th': @@ -186,7 +185,7 @@ def __conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, weight_deca x = Activation('relu')(x) if bottleneck: - inter_channel = nb_filter * 4 # Obtained from https://github.com/liuzhuang13/DenseNet/blob/master/densenet.lua + inter_channel = nb_filter * 4 # Obtained from https://github.com/liuzhuang13/DenseNet/blob/master/densenet.lua x = Convolution2D(inter_channel, 1, 1, init='he_uniform', border_mode='same', bias=False, W_regularizer=l2(weight_decay))(x) @@ -195,7 +194,7 @@ def __conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, weight_deca x = Dropout(dropout_rate)(x) x = BatchNormalization(mode=0, axis=concat_axis, gamma_regularizer=l2(weight_decay), - beta_regularizer=l2(weight_decay))(x) + beta_regularizer=l2(weight_decay))(x) x = Activation('relu')(x) x = Convolution2D(nb_filter, 3, 3, init="he_uniform", border_mode="same", bias=False, @@ -278,7 +277,6 @@ def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_bl reduction: reduction factor of transition blocks. Note : reduction value is inverted to compute compression dropout_rate: dropout rate weight_decay: weight decay - verbose: print the model type Returns: keras tensor with nb_layers of __conv_block appended