added missing hashes (#7988)

* added missing hashes * reverted changes to the example scripts
keras-team · Sep 26, 2017 · fb4a084 · fb4a084
1 parent 771be1a
commit fb4a084
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Showing 9 changed files with 32 additions and 16 deletions.
diff --git a/keras/applications/imagenet_utils.py b/keras/applications/imagenet_utils.py
@@ -83,7 +83,8 @@ def decode_predictions(preds, top=5):
     if CLASS_INDEX is None:
         fpath = get_file('imagenet_class_index.json',
                          CLASS_INDEX_PATH,
-                         cache_subdir='models')
+                         cache_subdir='models',
+                         file_hash='c2c37ea517e94d9795004a39431a14cb')
         CLASS_INDEX = json.load(open(fpath))
     results = []
     for pred in preds:

diff --git a/keras/applications/inception_resnet_v2.py b/keras/applications/inception_resnet_v2.py
@@ -362,13 +362,13 @@ def InceptionResNetV2(include_top=True,
             weights_path = get_file(weights_filename,
                                     BASE_WEIGHT_URL + weights_filename,
                                     cache_subdir='models',
-                                    md5_hash='e693bd0210a403b3192acc6073ad2e96')
+                                    file_hash='e693bd0210a403b3192acc6073ad2e96')
         else:
             weights_filename = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5'
             weights_path = get_file(weights_filename,
                                     BASE_WEIGHT_URL + weights_filename,
                                     cache_subdir='models',
-                                    md5_hash='d19885ff4a710c122648d3b5c3b684e4')
+                                    file_hash='d19885ff4a710c122648d3b5c3b684e4')
         model.load_weights(weights_path)
 
     return model
diff --git a/keras/applications/inception_v3.py b/keras/applications/inception_v3.py
@@ -377,13 +377,13 @@ def InceptionV3(include_top=True,
                 'inception_v3_weights_tf_dim_ordering_tf_kernels.h5',
                 WEIGHTS_PATH,
                 cache_subdir='models',
-                md5_hash='9a0d58056eeedaa3f26cb7ebd46da564')
+                file_hash='9a0d58056eeedaa3f26cb7ebd46da564')
         else:
             weights_path = get_file(
                 'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
                 WEIGHTS_PATH_NO_TOP,
                 cache_subdir='models',
-                md5_hash='bcbd6486424b2319ff4ef7d526e38f63')
+                file_hash='bcbd6486424b2319ff4ef7d526e38f63')
         model.load_weights(weights_path)
     return model
 

diff --git a/keras/applications/vgg16.py b/keras/applications/vgg16.py
@@ -162,11 +162,13 @@ def VGG16(include_top=True, weights='imagenet',
         if include_top:
             weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',
                                     WEIGHTS_PATH,
-                                    cache_subdir='models')
+                                    cache_subdir='models',
+                                    file_hash='64373286793e3c8b2b4e3219cbf3544b')
         else:
             weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                     WEIGHTS_PATH_NO_TOP,
-                                    cache_subdir='models')
+                                    cache_subdir='models',
+                                    file_hash='6d6bbae143d832006294945121d1f1fc')
         model.load_weights(weights_path)
         if K.backend() == 'theano':
             layer_utils.convert_all_kernels_in_model(model)

diff --git a/keras/applications/vgg19.py b/keras/applications/vgg19.py
@@ -165,11 +165,13 @@ def VGG19(include_top=True, weights='imagenet',
         if include_top:
             weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels.h5',
                                     WEIGHTS_PATH,
-                                    cache_subdir='models')
+                                    cache_subdir='models',
+                                    file_hash='cbe5617147190e668d6c5d5026f83318')
         else:
             weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                     WEIGHTS_PATH_NO_TOP,
-                                    cache_subdir='models')
+                                    cache_subdir='models',
+                                    file_hash='253f8cb515780f3b799900260a226db6')
         model.load_weights(weights_path)
         if K.backend() == 'theano':
             layer_utils.convert_all_kernels_in_model(model)

diff --git a/keras/applications/xception.py b/keras/applications/xception.py
@@ -249,11 +249,13 @@ def Xception(include_top=True, weights='imagenet',
         if include_top:
             weights_path = get_file('xception_weights_tf_dim_ordering_tf_kernels.h5',
                                     TF_WEIGHTS_PATH,
-                                    cache_subdir='models')
+                                    cache_subdir='models',
+                                    file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
         else:
             weights_path = get_file('xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                     TF_WEIGHTS_PATH_NO_TOP,
-                                    cache_subdir='models')
+                                    cache_subdir='models',
+                                    file_hash='b0042744bf5b25fce3cb969f33bebb97')
         model.load_weights(weights_path)
 
     if old_data_format:

diff --git a/keras/datasets/imdb.py b/keras/datasets/imdb.py
@@ -48,7 +48,9 @@ def load_data(path='imdb.npz', num_words=None, skip_top=0,
     if kwargs:
         raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
 
-    path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/imdb.npz')
+    path = get_file(path,
+                    origin='https://s3.amazonaws.com/text-datasets/imdb.npz',
+                    file_hash='599dadb1135973df5b59232a0e9a887c')
     with np.load(path) as f:
         x_train, labels_train = f['x_train'], f['y_train']
         x_test, labels_test = f['x_test'], f['y_test']
@@ -105,7 +107,8 @@ def get_word_index(path='imdb_word_index.json'):
         The word index dictionary.
     """
     path = get_file(path,
-                    origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json')
+                    origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json',
+                    file_hash='bfafd718b763782e994055a2d397834f')
     f = open(path)
     data = json.load(f)
     f.close()

diff --git a/keras/datasets/mnist.py b/keras/datasets/mnist.py
@@ -12,7 +12,9 @@ def load_data(path='mnist.npz'):
     # Returns
         Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
     """
-    path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.npz')
+    path = get_file(path,
+                    origin='https://s3.amazonaws.com/img-datasets/mnist.npz',
+                    file_hash='8a61469f7ea1b51cbae51d4f78837e45')
     f = np.load(path)
     x_train, y_train = f['x_train'], f['y_train']
     x_test, y_test = f['x_test'], f['y_test']

diff --git a/keras/datasets/reuters.py b/keras/datasets/reuters.py
@@ -46,7 +46,9 @@ def load_data(path='reuters.npz', num_words=None, skip_top=0,
     if kwargs:
         raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
 
-    path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/reuters.npz')
+    path = get_file(path,
+                    origin='https://s3.amazonaws.com/text-datasets/reuters.npz',
+                    file_hash='87aedbeb0cb229e378797a632c1997b6')
     with np.load(path) as f:
         xs, labels = f['x'], f['y']
 
@@ -90,7 +92,9 @@ def get_word_index(path='reuters_word_index.json'):
     # Returns
         The word index dictionary.
     """
-    path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/reuters_word_index.json')
+    path = get_file(path,
+                    origin='https://s3.amazonaws.com/text-datasets/reuters_word_index.json',
+                    file_hash='4d44cc38712099c9e383dc6e5f11a921')
     f = open(path)
     data = json.load(f)
     f.close()