Style fix (avoid use of reserved variable name).
@@ -515,11 +515,11 @@ def MobileNet(input_shape=None,
return model
-def _conv_block(input, filters, alpha, kernel=(3, 3), strides=(1, 1)):
+def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
"""Adds an initial convolution layer (with batch normalization and relu6).
# Arguments
- input: Input tensor of shape `(rows, cols, 3)`
+ inputs: Input tensor of shape `(rows, cols, 3)`
(with `channels_last` data format) or
(3, rows, cols) (with `channels_first` data format).
It should have exactly 3 inputs channels,
@@ -567,12 +567,12 @@ def _conv_block(input, filters, alpha, kernel=(3, 3), strides=(1, 1)):
padding='same',
use_bias=False,
strides=strides,
- name='conv1')(input)
+ name='conv1')(inputs)
x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x)
return Activation(relu6, name='conv1_relu')(x)
-def _depthwise_conv_block(input, pointwise_conv_filters, alpha,
+def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha,
depth_multiplier=1, strides=(1, 1), block_id=1):
"""Adds a depthwise convolution block.
@@ -581,7 +581,7 @@ def _depthwise_conv_block(input, pointwise_conv_filters, alpha,
batch normalization and relu6 activation.
# Arguments
- input: Input tensor of shape `(rows, cols, channels)`
+ inputs: Input tensor of shape `(rows, cols, channels)`
(with `channels_last` data format) or
(channels, rows, cols) (with `channels_first` data format).
pointwise_conv_filters: Integer, the dimensionality of the output space
@@ -629,7 +629,7 @@ def _depthwise_conv_block(input, pointwise_conv_filters, alpha,
depth_multiplier=depth_multiplier,
strides=strides,
use_bias=False,
- name='conv_dw_%d' % block_id)(input)
+ name='conv_dw_%d' % block_id)(inputs)
x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x)
x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x)
