Fix NASNet (#10209)

* Fix NASNet

* Update weight files

keras/applications/nasnet.py

@@ -60,10 +60,10 @@ from ..applications.inception_v3 import preprocess_input
 from ..applications.imagenet_utils import decode_predictions
 from .. import backend as K
 
-NASNET_MOBILE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-mobile.h5'
-NASNET_MOBILE_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-mobile-no-top.h5'
-NASNET_LARGE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-large.h5'
-NASNET_LARGE_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-large-no-top.h5'
+NASNET_MOBILE_WEIGHT_PATH = 'https://github.com/titu1994/Keras-NASNet/releases/download/v1.2/NASNet-mobile.h5'
+NASNET_MOBILE_WEIGHT_PATH_NO_TOP = 'https://github.com/titu1994/Keras-NASNet/releases/download/v1.2/NASNet-mobile-no-top.h5'
+NASNET_LARGE_WEIGHT_PATH = 'https://github.com/titu1994/Keras-NASNet/releases/download/v1.2/NASNet-large.h5'
+NASNET_LARGE_WEIGHT_PATH_NO_TOP = 'https://github.com/titu1994/Keras-NASNet/releases/download/v1.2/NASNet-large-no-top.h5'
 
 
 def NASNet(input_shape=None,
@@ -102,7 +102,7 @@ def NASNet(input_shape=None,
                 -   P is the number of penultimate filters
         stem_block_filters: Number of filters in the initial stem block
         skip_reduction: Whether to skip the reduction step at the tail
-            end of the network. Set to `False` for CIFAR models.
+            end of the network.
         filter_multiplier: Controls the width of the network.
             - If `filter_multiplier` < 1.0, proportionally decreases the number
                 of filters in each layer.
@@ -210,24 +210,18 @@ def NASNet(input_shape=None,
     channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
     filters = penultimate_filters // 24
 
-    if not skip_reduction:
-        x = Conv2D(stem_block_filters, (3, 3), strides=(2, 2), padding='valid',
-                   use_bias=False, name='stem_conv1',
-                   kernel_initializer='he_normal')(img_input)
-    else:
-        x = Conv2D(stem_block_filters, (3, 3), strides=(1, 1), padding='same',
-                   use_bias=False, name='stem_conv1',
-                   kernel_initializer='he_normal')(img_input)
+    x = Conv2D(stem_block_filters, (3, 3), strides=(2, 2), padding='valid',
+               use_bias=False, name='stem_conv1',
+               kernel_initializer='he_normal')(img_input)
 
     x = BatchNormalization(axis=channel_dim, momentum=0.9997,
                            epsilon=1e-3, name='stem_bn1')(x)
 
     p = None
-    if not skip_reduction:  # imagenet / mobile mode
-        x, p = _reduction_a_cell(x, p, filters // (filter_multiplier ** 2),
-                                 block_id='stem_1')
-        x, p = _reduction_a_cell(x, p, filters // filter_multiplier,
-                                 block_id='stem_2')
+    x, p = _reduction_a_cell(x, p, filters // (filter_multiplier ** 2),
+                             block_id='stem_1')
+    x, p = _reduction_a_cell(x, p, filters // filter_multiplier,
+                             block_id='stem_2')
 
     for i in range(num_blocks):
         x, p = _normal_a_cell(x, p, filters, block_id='%d' % (i))
@@ -274,27 +268,32 @@ def NASNet(input_shape=None,
     if weights == 'imagenet':
         if default_size == 224:  # mobile version
             if include_top:
-                weight_path = NASNET_MOBILE_WEIGHT_PATH
-                model_name = 'nasnet_mobile.h5'
+                weights_path = get_file(
+                    'nasnet_mobile.h5',
+                    NASNET_MOBILE_WEIGHT_PATH,
+                    cache_subdir='models',
+                    file_hash='020fb642bf7360b370c678b08e0adf61')
             else:
-                weight_path = NASNET_MOBILE_WEIGHT_PATH_NO_TOP
-                model_name = 'nasnet_mobile_no_top.h5'
-
-            weights_file = get_file(model_name, weight_path,
-                                    cache_subdir='models')
-            model.load_weights(weights_file)
-
+                weights_path = get_file(
+                    'nasnet_mobile_no_top.h5',
+                    NASNET_MOBILE_WEIGHT_PATH_NO_TOP,
+                    cache_subdir='models',
+                    file_hash='1ed92395b5b598bdda52abe5c0dbfd63')
+            model.load_weights(weights_path)
         elif default_size == 331:  # large version
             if include_top:
-                weight_path = NASNET_LARGE_WEIGHT_PATH
-                model_name = 'nasnet_large.h5'
+                weights_path = get_file(
+                    'nasnet_large.h5',
+                    NASNET_LARGE_WEIGHT_PATH,
+                    cache_subdir='models',
+                    file_hash='11577c9a518f0070763c2b964a382f17')
             else:
-                weight_path = NASNET_LARGE_WEIGHT_PATH_NO_TOP
-                model_name = 'nasnet_large_no_top.h5'
-
-            weights_file = get_file(model_name, weight_path,
-                                    cache_subdir='models')
-            model.load_weights(weights_file)
+                weights_path = get_file(
+                    'nasnet_large_no_top.h5',
+                    NASNET_LARGE_WEIGHT_PATH_NO_TOP,
+                    cache_subdir='models',
+                    file_hash='d81d89dc07e6e56530c4e77faddd61b5')
+            model.load_weights(weights_path)
         else:
             raise ValueError(
                 'ImageNet weights can only be loaded with NASNetLarge'
@@ -364,7 +363,7 @@ def NASNetLarge(input_shape=None,
                   penultimate_filters=4032,
                   num_blocks=6,
                   stem_block_filters=96,
-                  skip_reduction=False,
+                  skip_reduction=True,
                   filter_multiplier=2,
                   include_top=include_top,
                   weights=weights,
@@ -630,7 +629,7 @@ def _reduction_a_cell(ip, p, filters, block_id=None):
             x1_1 = _separable_conv_block(h, filters, (5, 5), strides=(2, 2),
                                          block_id='reduction_left1_%s' % block_id)
             x1_2 = _separable_conv_block(p, filters, (7, 7), strides=(2, 2),
-                                         block_id='reduction_1_%s' % block_id)
+                                         block_id='reduction_right1_%s' % block_id)
             x1 = add([x1_1, x1_2], name='reduction_add_1_%s' % block_id)
 
         with K.name_scope('block_2'):