ValueError: in case of invalid argument for `weights`,
or invalid input shape.
if weights not in {"imagenet", None}:
raise ValueError("The `weights` argument should be either "
"`None` (random initialization) or `imagenet` "
"(pre-training on ImageNet).")
After Change
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
if not (weights in {"imagenet", None} or os.path.exists(weights)):
raise ValueError("The `weights` argument should be either "
"`None` (random initialization), `imagenet` "
"(pre-training on ImageNet), "
"or the path to the weights file to be loaded.")
if weights == "imagenet" and include_top and classes != 1000:
raise ValueError("If using `weights` as imagenet with `include_top`"
" as true, `classes` should be 1000")
// Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=197,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if K.image_data_format() == "channels_last":
bn_axis = 3
else:
bn_axis = 1
x = Conv2D(
64, (7, 7), strides=(2, 2), padding="same", name="conv1")(img_input)
x = BatchNormalization(axis=bn_axis, name="bn_conv1")(x)
x = Activation("relu")(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block="a", strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block="b")
x = identity_block(x, 3, [64, 64, 256], stage=2, block="c")
x = conv_block(x, 3, [128, 128, 512], stage=3, block="a")
x = identity_block(x, 3, [128, 128, 512], stage=3, block="b")
x = identity_block(x, 3, [128, 128, 512], stage=3, block="c")
x = identity_block(x, 3, [128, 128, 512], stage=3, block="d")
x = conv_block(x, 3, [256, 256, 1024], stage=4, block="a")
x = identity_block(x, 3, [256, 256, 1024], stage=4, block="b")
x = identity_block(x, 3, [256, 256, 1024], stage=4, block="c")
x = identity_block(x, 3, [256, 256, 1024], stage=4, block="d")
x = identity_block(x, 3, [256, 256, 1024], stage=4, block="e")
x = identity_block(x, 3, [256, 256, 1024], stage=4, block="f")
x = conv_block(x, 3, [512, 512, 2048], stage=5, block="a")
x = identity_block(x, 3, [512, 512, 2048], stage=5, block="b")
x = identity_block(x, 3, [512, 512, 2048], stage=5, block="c")
x = AveragePooling2D((7, 7), name="avg_pool")(x)
if include_top:
x = Flatten()(x)
x = Dense(classes, activation="softmax", name="fc1000")(x)
else:
if pooling == "avg":
x = GlobalAveragePooling2D()(x)
elif pooling == "max":
x = GlobalMaxPooling2D()(x)
// Ensure that the model takes into account
// any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
// Create model.
model = Model(inputs, x, name="resnet50")
// load weights
if weights == "imagenet":
if include_top:
weights_path = get_file("resnet50_weights_tf_dim_ordering_tf_kernels.h5",
WEIGHTS_PATH,
cache_subdir="models",
md5_hash="a7b3fe01876f51b976af0dea6bc144eb")
else:
weights_path = get_file("resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5",
WEIGHTS_PATH_NO_TOP,
cache_subdir="models",
md5_hash="a268eb855778b3df3c7506639542a6af")
model.load_weights(weights_path)
if K.backend() == "theano":
layer_utils.convert_all_kernels_in_model(model)
if include_top:
maxpool = model.get_layer(name="avg_pool")
shape = maxpool.output_shape[1:]
dense = model.get_layer(name="fc1000")
layer_utils.convert_dense_weights_data_format(dense, shape, "channels_first")
if K.image_data_format() == "channels_first" and K.backend() == "tensorflow":
warnings.warn("You are using the TensorFlow backend, yet you "
"are using the Theano "
"image data format convention "
"(`image_data_format="channels_first"`). "
"For best performance, set "
"`image_data_format="channels_last"` in "
"your Keras config "
"at ~/.keras/keras.json.")
elif weights is not None:
model.load_weights(weights)
return model
