Python keras.backend.is_keras_tensor() Examples
The following are 25
code examples of keras.backend.is_keras_tensor().
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Example #1
| Source File: jaccard_test.py From keras-contrib with MIT License | 6 votes |
|
def test_jaccard_distance():
# all_right, almost_right, half_right, all_wrong
y_true = np.array([[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0],
[0, 0, 1., 0.]])
y_pred = np.array([[0, 0, 1, 0], [0, 0, 0.9, 0], [0, 0, 0.1, 0],
[1, 1, 0.1, 1.]])
r = jaccard_distance(
K.variable(y_true),
K.variable(y_pred), )
if K.is_keras_tensor(r):
assert K.int_shape(r) == (4, )
all_right, almost_right, half_right, all_wrong = K.eval(r)
assert all_right == 0, 'should converge on zero'
assert all_right < almost_right
assert almost_right < half_right
assert half_right < all_wrong
Example #2
| Source File: metrics.py From intent_classifier with Apache License 2.0 | 5 votes |
|
def fmeasure(y_true, y_pred):
try:
_ = K.is_keras_tensor(y_pred)
return fbeta_score_K(y_true, y_pred, beta=1)
except ValueError:
return fbeta_score_np(y_true, y_pred, beta=1)
Example #3
| Source File: linknet.py From Keras-LinkNet with MIT License | 5 votes |
|
def __init__(
self,
num_classes,
input_tensor=None,
input_shape=None,
initial_block_filters=64,
bias=False,
name='linknet'
):
self.num_classes = num_classes
self.initial_block_filters = initial_block_filters
self.bias = bias
self.output_shape = input_shape[:-1] + (num_classes, )
# Create a Keras tensor from the input_shape/input_tensor
if input_tensor is None:
self.input = Input(shape=input_shape, name='input_img')
elif is_keras_tensor(input_tensor):
self.input = input_tensor
else:
# input_tensor is a tensor but not one from Keras
self.input = Input(
tensor=input_tensor, shape=input_shape, name='input_img'
)
self.name = name
Example #4
| Source File: grud_layers.py From GRU-D with MIT License | 5 votes |
|
def __call__(self, inputs, initial_state=None, **kwargs):
# We skip `__call__` of `RNN` and `GRU` in this case and directly execute
# GRUD's great-grandparent's method.
inputs, initial_state = _standardize_grud_args(inputs, initial_state)
if initial_state is None:
return super(RNN, self).__call__(inputs, **kwargs)
# If `initial_state` is specified and is Keras
# tensors, then add it to the inputs and temporarily modify the
# input_spec to include them.
additional_inputs = []
additional_specs = []
kwargs['initial_state'] = initial_state
additional_inputs += initial_state
self.state_spec = [InputSpec(shape=K.int_shape(state))
for state in initial_state]
additional_specs += self.state_spec
# at this point additional_inputs cannot be empty
is_keras_tensor = K.is_keras_tensor(additional_inputs[0])
for tensor in additional_inputs:
if K.is_keras_tensor(tensor) != is_keras_tensor:
raise ValueError('The initial state or constants of an RNN'
' layer cannot be specified with a mix of'
' Keras tensors and non-Keras tensors'
' (a "Keras tensor" is a tensor that was'
' returned by a Keras layer, or by `Input`)')
if is_keras_tensor:
# Compute the full input spec, including state and constants
full_input = inputs + additional_inputs
full_input_spec = self.input_spec + additional_specs
# Perform the call with temporarily replaced input_spec
original_input_spec = self.input_spec
self.input_spec = full_input_spec
output = super(RNN, self).__call__(full_input, **kwargs)
self.input_spec = original_input_spec
return output
return super(RNN, self).__call__(inputs, **kwargs)
Example #5
| Source File: resnet101.py From keras-faster-rcnn with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable=trainable)(x)
x = FixedBatchNormalization(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), trainable=trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable=trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable=trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable=trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable=trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable=trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable=trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='g', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='h', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='i', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='j', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='k', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='l', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='m', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='n', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='o', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='p', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='q', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='r', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='s', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='t', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='u', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='v', trainable=trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='w', trainable=trainable)
return x
Example #6
| Source File: resnet.py From ZSD_Release with MIT License | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x)
x = FixedBatchNormalization(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), trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)
return x
Example #7
| Source File: dilation_net.py From dilation-keras with MIT License | 4 votes |
|
def get_dilation_model_camvid(input_shape, apply_softmax, input_tensor, classes):
if input_tensor is None:
model_in = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
model_in = Input(tensor=input_tensor, shape=input_shape)
else:
model_in = input_tensor
h = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')(model_in)
h = Convolution2D(64, 3, 3, activation='relu', name='conv1_2')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1')(h)
h = Convolution2D(128, 3, 3, activation='relu', name='conv2_1')(h)
h = Convolution2D(128, 3, 3, activation='relu', name='conv2_2')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool2')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_1')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_2')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_3')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool3')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_1')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_2')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_3')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_1')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_2')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_3')(h)
h = AtrousConvolution2D(4096, 7, 7, atrous_rate=(4, 4), activation='relu', name='fc6')(h)
h = Dropout(0.5, name='drop6')(h)
h = Convolution2D(4096, 1, 1, activation='relu', name='fc7')(h)
h = Dropout(0.5, name='drop7')(h)
h = Convolution2D(classes, 1, 1, name='final')(h)
h = ZeroPadding2D(padding=(1, 1))(h)
h = Convolution2D(classes, 3, 3, activation='relu', name='ctx_conv1_1')(h)
h = ZeroPadding2D(padding=(1, 1))(h)
h = Convolution2D(classes, 3, 3, activation='relu', name='ctx_conv1_2')(h)
h = ZeroPadding2D(padding=(2, 2))(h)
h = AtrousConvolution2D(classes, 3, 3, atrous_rate=(2, 2), activation='relu', name='ctx_conv2_1')(h)
h = ZeroPadding2D(padding=(4, 4))(h)
h = AtrousConvolution2D(classes, 3, 3, atrous_rate=(4, 4), activation='relu', name='ctx_conv3_1')(h)
h = ZeroPadding2D(padding=(8, 8))(h)
h = AtrousConvolution2D(classes, 3, 3, atrous_rate=(8, 8), activation='relu', name='ctx_conv4_1')(h)
h = ZeroPadding2D(padding=(16, 16))(h)
h = AtrousConvolution2D(classes, 3, 3, atrous_rate=(16, 16), activation='relu', name='ctx_conv5_1')(h)
h = ZeroPadding2D(padding=(1, 1))(h)
h = Convolution2D(classes, 3, 3, activation='relu', name='ctx_fc1')(h)
logits = Convolution2D(classes, 1, 1, name='ctx_final')(h)
if apply_softmax:
model_out = softmax(logits)
else:
model_out = logits
model = Model(input=model_in, output=model_out, name='dilation_camvid')
return model
# model function
Example #8
| Source File: dilation_net.py From dilation-keras with MIT License | 4 votes |
|
def get_dilation_model_kitti(input_shape, apply_softmax, input_tensor, classes):
if input_tensor is None:
model_in = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
model_in = Input(tensor=input_tensor, shape=input_shape)
else:
model_in = input_tensor
h = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')(model_in)
h = Convolution2D(64, 3, 3, activation='relu', name='conv1_2')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1')(h)
h = Convolution2D(128, 3, 3, activation='relu', name='conv2_1')(h)
h = Convolution2D(128, 3, 3, activation='relu', name='conv2_2')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool2')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_1')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_2')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_3')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool3')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_1')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_2')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_3')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_1')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_2')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_3')(h)
h = AtrousConvolution2D(4096, 7, 7, atrous_rate=(4, 4), activation='relu', name='fc6')(h)
h = Dropout(0.5, name='drop6')(h)
h = Convolution2D(4096, 1, 1, activation='relu', name='fc7')(h)
h = Dropout(0.5, name='drop7')(h)
h = Convolution2D(classes, 1, 1, name='final')(h)
h = ZeroPadding2D(padding=(1, 1))(h)
h = Convolution2D(classes, 3, 3, activation='relu', name='ctx_conv1_1')(h)
h = ZeroPadding2D(padding=(1, 1))(h)
h = Convolution2D(classes, 3, 3, activation='relu', name='ctx_conv1_2')(h)
h = ZeroPadding2D(padding=(2, 2))(h)
h = AtrousConvolution2D(classes, 3, 3, atrous_rate=(2, 2), activation='relu', name='ctx_conv2_1')(h)
h = ZeroPadding2D(padding=(4, 4))(h)
h = AtrousConvolution2D(classes, 3, 3, atrous_rate=(4, 4), activation='relu', name='ctx_conv3_1')(h)
h = ZeroPadding2D(padding=(8, 8))(h)
h = AtrousConvolution2D(classes, 3, 3, atrous_rate=(8, 8), activation='relu', name='ctx_conv4_1')(h)
h = ZeroPadding2D(padding=(1, 1))(h)
h = Convolution2D(classes, 3, 3, activation='relu', name='ctx_fc1')(h)
logits = Convolution2D(classes, 1, 1, name='ctx_final')(h)
if apply_softmax:
model_out = softmax(logits)
else:
model_out = logits
model = Model(input=model_in, output=model_out, name='dilation_kitti')
return model
# CAMVID MODEL
Example #9
| Source File: dilation_net.py From dilation-keras with MIT License | 4 votes |
|
def get_dilation_model_voc(input_shape, apply_softmax, input_tensor, classes):
if input_tensor is None:
model_in = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
model_in = Input(tensor=input_tensor, shape=input_shape)
else:
model_in = input_tensor
h = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')(model_in)
h = Convolution2D(64, 3, 3, activation='relu', name='conv1_2')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1')(h)
h = Convolution2D(128, 3, 3, activation='relu', name='conv2_1')(h)
h = Convolution2D(128, 3, 3, activation='relu', name='conv2_2')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool2')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_1')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_2')(h)
h = Convolution2D(256, 3, 3, activation='relu', name='conv3_3')(h)
h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool3')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_1')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_2')(h)
h = Convolution2D(512, 3, 3, activation='relu', name='conv4_3')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_1')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_2')(h)
h = AtrousConvolution2D(512, 3, 3, atrous_rate=(2, 2), activation='relu', name='conv5_3')(h)
h = AtrousConvolution2D(4096, 7, 7, atrous_rate=(4, 4), activation='relu', name='fc6')(h)
h = Dropout(0.5, name='drop6')(h)
h = Convolution2D(4096, 1, 1, activation='relu', name='fc7')(h)
h = Dropout(0.5, name='drop7')(h)
h = Convolution2D(classes, 1, 1, activation='relu', name='fc-final')(h)
h = ZeroPadding2D(padding=(33, 33))(h)
h = Convolution2D(2 * classes, 3, 3, activation='relu', name='ct_conv1_1')(h)
h = Convolution2D(2 * classes, 3, 3, activation='relu', name='ct_conv1_2')(h)
h = AtrousConvolution2D(4 * classes, 3, 3, atrous_rate=(2, 2), activation='relu', name='ct_conv2_1')(h)
h = AtrousConvolution2D(8 * classes, 3, 3, atrous_rate=(4, 4), activation='relu', name='ct_conv3_1')(h)
h = AtrousConvolution2D(16 * classes, 3, 3, atrous_rate=(8, 8), activation='relu', name='ct_conv4_1')(h)
h = AtrousConvolution2D(32 * classes, 3, 3, atrous_rate=(16, 16), activation='relu', name='ct_conv5_1')(h)
h = Convolution2D(32 * classes, 3, 3, activation='relu', name='ct_fc1')(h)
logits = Convolution2D(classes, 1, 1, name='ct_final')(h)
if apply_softmax:
model_out = softmax(logits)
else:
model_out = logits
model = Model(input=model_in, output=model_out, name='dilation_voc12')
return model
# KITTI MODEL
Example #10
| Source File: resnet.py From ssbm_fox_detector with MIT License | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x)
x = FixedBatchNormalization(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), trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)
return x
Example #11
| Source File: vgg.py From ssbm_fox_detector with MIT License | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
return x
Example #12
| Source File: resnet.py From keras-frcnn with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None, trainable = False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1', trainable = trainable)(x)
x = FixedBatchNormalization(trainable=False,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), trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)
return x
Example #13
| Source File: densenet_models.py From EvadeML-Zoo with MIT License | 4 votes |
|
def densenet_cifar10_model(logits=False, input_range_type=1, pre_filter=lambda x:x):
assert input_range_type == 1
batch_size = 64
nb_classes = 10
img_rows, img_cols = 32, 32
img_channels = 3
img_dim = (img_channels, img_rows, img_cols) if K.image_dim_ordering() == "th" else (img_rows, img_cols, img_channels)
depth = 40
nb_dense_block = 3
growth_rate = 12
nb_filter = 16
dropout_rate = 0.0 # 0.0 for data augmentation
input_tensor = None
include_top=True
if logits is True:
activation = None
else:
activation = "softmax"
# Determine proper input shape
input_shape = _obtain_input_shape(img_dim,
default_size=32,
min_size=8,
data_format=K.image_data_format(),
include_top=include_top)
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
x = __create_dense_net(nb_classes, img_input, True, depth, nb_dense_block,
growth_rate, nb_filter, -1, False, 0.0,
dropout_rate, 1E-4, activation)
# 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='densenet')
return model
# Source: https://github.com/titu1994/DenseNet
Example #14
| Source File: resnet.py From keras-faster-rcnn with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x)
x = FixedBatchNormalization(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), trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)
return x
Example #15
| Source File: vgg.py From keras-faster-rcnn with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
return x
Example #16
| Source File: attention.py From keras-utility-layer-collection with MIT License | 4 votes |
|
def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
inputs, initial_state, constants = self._standardize_args(
inputs, initial_state, constants, self._num_constants)
if initial_state is None and constants is None:
return super(ExternalAttentionRNNWrapper, self).__call__(inputs, **kwargs)
# If any of `initial_state` or `constants` are specified and are Keras
# tensors, then add them to the inputs and temporarily modify the
# input_spec to include them.
additional_inputs = []
additional_specs = []
if initial_state is not None:
kwargs['initial_state'] = initial_state
additional_inputs += initial_state
self.state_spec = [InputSpec(shape=K.int_shape(state))
for state in initial_state]
additional_specs += self.state_spec
if constants is not None:
kwargs['constants'] = constants
additional_inputs += constants
self.constants_spec = [InputSpec(shape=K.int_shape(constant))
for constant in constants]
self._num_constants = len(constants)
additional_specs += self.constants_spec
# at this point additional_inputs cannot be empty
is_keras_tensor = K.is_keras_tensor(additional_inputs[0])
for tensor in additional_inputs:
if K.is_keras_tensor(tensor) != is_keras_tensor:
raise ValueError('The initial state or constants of an ExternalAttentionRNNWrapper'
' layer cannot be specified with a mix of'
' Keras tensors and non-Keras tensors'
' (a "Keras tensor" is a tensor that was'
' returned by a Keras layer, or by `Input`)')
if is_keras_tensor:
# Compute the full input spec, including state and constants
full_input = inputs + additional_inputs
full_input_spec = self.input_spec + additional_specs
# Perform the call with temporarily replaced input_spec
original_input_spec = self.input_spec
self.input_spec = full_input_spec
output = super(ExternalAttentionRNNWrapper, self).__call__(full_input, **kwargs)
self.input_spec = self.input_spec[:len(original_input_spec)]
return output
else:
return super(ExternalAttentionRNNWrapper, self).__call__(inputs, **kwargs)
Example #17
| Source File: dcn_vgg.py From sam with MIT License | 4 votes |
|
def dcn_vgg(input_tensor=None):
input_shape = (3, None, None)
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
# conv_1
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# conv_2
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)
x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# conv_3
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)
x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', border_mode='same')(x)
# conv_4
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)
x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(1, 1), name='block4_pool', border_mode='same')(x)
# conv_5
x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1', atrous_rate=(2, 2))(x)
x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2', atrous_rate=(2, 2))(x)
x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3', atrous_rate=(2, 2))(x)
# Create model
model = Model(img_input, x)
# Load weights
weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5', TH_WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
return model
Example #18
| Source File: dcn_resnet.py From sam with MIT License | 4 votes |
|
def dcn_resnet(input_tensor=None):
input_shape = (3, None, None)
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)
else:
img_input = input_tensor
bn_axis = 1
# conv_1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), border_mode='same')(x)
# conv_2
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')
# conv_3
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', strides=(2, 2))
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')
# conv_4
x = conv_block_atrous(x, 3, [256, 256, 1024], stage=4, block='a', atrous_rate=(2, 2))
x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='b', atrous_rate=(2, 2))
x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='c', atrous_rate=(2, 2))
x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='d', atrous_rate=(2, 2))
x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='e', atrous_rate=(2, 2))
x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='f', atrous_rate=(2, 2))
# conv_5
x = conv_block_atrous(x, 3, [512, 512, 2048], stage=5, block='a', atrous_rate=(4, 4))
x = identity_block_atrous(x, 3, [512, 512, 2048], stage=5, block='b', atrous_rate=(4, 4))
x = identity_block_atrous(x, 3, [512, 512, 2048], stage=5, block='c', atrous_rate=(4, 4))
# Create model
model = Model(img_input, x)
# Load weights
weights_path = get_file('resnet50_weights_th_dim_ordering_th_kernels_notop.h5', TH_WEIGHTS_PATH_NO_TOP,
cache_subdir='models', md5_hash='f64f049c92468c9affcd44b0976cdafe')
model.load_weights(weights_path)
return model
Example #19
| Source File: nn_arch_resnet50.py From Keras_object_detection with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None, trainable=True):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x)
x = FixedBatchNormalization(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), trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)
return x
Example #20
| Source File: nn_arch_vgg16.py From Keras_object_detection with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None,trainable=True):
""" The architecture of VGG-16 (fixed feature extractor)
Takes input_tensor as optional argument
# do not change the arguments of the function
NOTE: Make sure to give names for all the layers. These names will be
used to freeze the corresponding layers if doing 4-step alternating training
"""
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1',trainable=trainable)(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2',trainable=trainable)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool',trainable=trainable)(x) # max pooling has no trainbale layers
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1',trainable=trainable)(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2',trainable=trainable)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool',trainable=trainable)(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1',trainable=trainable)(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2',trainable=trainable)(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3',trainable=trainable)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool',trainable=trainable)(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1',trainable=trainable)(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2',trainable=trainable)(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3',trainable=trainable)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool',trainable=trainable)(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1',trainable=trainable)(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2',trainable=trainable)(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3',trainable=trainable)(x)
return(x)
Example #21
| Source File: resnet.py From Keras-FasterRCNN with MIT License | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x)
x = FixedBatchNormalization(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), trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)
return x
Example #22
| Source File: vgg.py From Keras-FasterRCNN with MIT License | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
return x
Example #23
| Source File: resnet.py From keras-frcnn with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x)
x = FixedBatchNormalization(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), trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)
return x
Example #24
| Source File: vgg.py From keras-frcnn with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
return x
Example #25
| Source File: resnet.py From FasterRCNN_KERAS with Apache License 2.0 | 4 votes |
|
def nn_base(input_tensor=None, trainable=False):
# Determine proper input shape
if K.image_dim_ordering() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
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_dim_ordering() == 'tf':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x)
x = FixedBatchNormalization(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), trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)
return x
