Python keras.backend.cast_to_floatx() Examples
The following are 30
code examples of keras.backend.cast_to_floatx().
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Example #1
| Source File: layers.py From research with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def get_constants(self, x):
constants = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.concatenate([ones] * self.output_dim, 1)
B_U = K.in_train_phase(K.dropout(ones, self.dropout_U), ones)
constants.append(B_U)
else:
constants.append(K.cast_to_floatx(1.))
if self.consume_less == 'cpu' and 0 < self.dropout_W < 1:
input_shape = self.input_spec[0].shape
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.concatenate([ones] * input_dim, 1)
B_W = K.in_train_phase(K.dropout(ones, self.dropout_W), ones)
constants.append(B_W)
else:
constants.append(K.cast_to_floatx(1.))
return constants
Example #2
| Source File: rtn.py From ikelos with MIT License | 6 votes |
|
def get_constants(self, x):
constants = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.concatenate([ones] * self.output_dim, 1)
B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(4)]
constants.append(B_U)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(4)])
if 0 < self.dropout_W < 1:
input_shape = self.input_spec[0].shape
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.concatenate([ones] * input_dim, 1)
B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(4)]
constants.append(B_W)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(4)])
return constants
Example #3
| Source File: rtn.py From ikelos with MIT License | 6 votes |
|
def get_constants(self, x):
constants = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.concatenate([ones] * self.output_dim, 1)
B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)]
constants.append(B_U)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
if 0 < self.dropout_W < 1:
input_shape = self.input_spec[0].shape
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.concatenate([ones] * input_dim, 1)
B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)]
constants.append(B_W)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #4
| Source File: rnnlayer.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 6 votes |
|
def get_constants(self, inputs, training=None):
constants = []
'''if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)]
constants.append(B_U)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
if 0 < self.dropout_W < 1:
input_shape = K.int_shape(x)
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)]
constants.append(B_W)
else:'''
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #5
| Source File: rnnlayer.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 6 votes |
|
def get_constants(self, inputs, training=None):
constants = []
'''if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)]
constants.append(B_U)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
if 0 < self.dropout_W < 1:
input_shape = K.int_shape(x)
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)]
constants.append(B_W)
else:'''
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #6
| Source File: rnnlayer.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 6 votes |
|
def get_constants(self, inputs, training=None):
constants = []
'''if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)]
constants.append(B_U)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
if 0 < self.dropout_W < 1:
input_shape = K.int_shape(x)
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)]
constants.append(B_W)
else:'''
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #7
| Source File: QnA.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 6 votes |
|
def get_constants(self, x):
constants = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.output_dim))
B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)]
constants.append(B_U)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
if 0 < self.dropout_W < 1:
input_shape = K.int_shape(x)
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)]
constants.append(B_W)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #8
| Source File: layers.py From research with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def get_constants(self, x):
constants = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.concatenate([ones] * self.output_dim, 1)
B_U = K.in_train_phase(K.dropout(ones, self.dropout_U), ones)
constants.append(B_U)
else:
constants.append(K.cast_to_floatx(1.))
if self.consume_less == 'cpu' and 0 < self.dropout_W < 1:
input_shape = self.input_spec[0].shape
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.concatenate([ones] * input_dim, 1)
B_W = K.in_train_phase(K.dropout(ones, self.dropout_W), ones)
constants.append(B_W)
else:
constants.append(K.cast_to_floatx(1.))
return constants
Example #9
| Source File: TTRNN.py From TT_RNN with MIT License | 6 votes |
|
def get_constants(self, inputs, training=None):
constants = []
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
if 0. < self.recurrent_dropout < 1:
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
def dropped_inputs():
return K.dropout(ones, self.recurrent_dropout)
rec_dp_mask = [K.in_train_phase(dropped_inputs,
ones,
training=training) for _ in range(3)]
constants.append(rec_dp_mask)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #10
| Source File: lstm2ntm.py From NTM-Keras with MIT License | 6 votes |
|
def get_constants(self, x):
constants = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.output_dim))
B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(4)]
constants.append(B_U)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(4)])
if 0 < self.dropout_W < 1:
input_shape = self.input_spec[0].shape
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(4)]
constants.append(B_W)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(4)])
return constants
Example #11
| Source File: tgru_k2_gpu.py From chemical_vae with Apache License 2.0 | 6 votes |
|
def get_constants(self, inputs, training=None):
constants = []
if 0. < self.recurrent_dropout < 1.:
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
def dropped_inputs():
return K.dropout(ones, self.recurrent_dropout)
rec_dp_mask = [K.in_train_phase(dropped_inputs,
ones,
training=training) for _ in range(3)]
constants.append(rec_dp_mask)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #12
| Source File: recurrent.py From keras_bn_library with MIT License | 6 votes |
|
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.input_dim)) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(4)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) if 0 < self.dropout_W < 1: input_shape = K.int_shape(x) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(4)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) return constants
Example #13
| Source File: rnnrbm.py From keras_bn_library with MIT License | 6 votes |
|
def get_constants(self, x):
constants = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.hidden_recurrent_dim))
B_U = K.in_train_phase(K.dropout(ones, self.dropout_U), ones)
constants.append(B_U)
else:
constants.append(K.cast_to_floatx(1.))
if self.consume_less == 'cpu' and 0 < self.dropout_W < 1:
input_shape = self.input_spec[0].shape
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, input_dim))
B_W = K.in_train_phase(K.dropout(ones, self.dropout_W), ones)
constants.append(B_W)
else:
constants.append(K.cast_to_floatx(1.))
return constants
Example #14
| Source File: rhn.py From deep-models with Apache License 2.0 | 6 votes |
|
def get_constants(self, x):
constants = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.output_dim))
B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)]
constants.append(B_U)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
if 0 < self.dropout_W < 1:
input_shape = self.input_spec[0].shape
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, input_dim))
B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)]
constants.append(B_W)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #15
| Source File: TTRNN.py From TT_RNN with MIT License | 5 votes |
|
def get_constants(self, inputs, training=None):
constants = []
if self.implementation != 0 and 0. < self.dropout < 1.:
input_shape = K.int_shape(inputs)
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
def dropped_inputs():
return K.dropout(ones, self.dropout)
dp_mask = K.in_train_phase(dropped_inputs,
ones,
training=training)
constants.append(dp_mask)
else:
constants.append(K.cast_to_floatx(1.))
if 0. < self.recurrent_dropout < 1.:
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
def dropped_inputs():
return K.dropout(ones, self.recurrent_dropout)
rec_dp_mask = K.in_train_phase(dropped_inputs,
ones,
training=training)
constants.append(rec_dp_mask)
else:
constants.append(K.cast_to_floatx(1.))
return constants
Example #16
| Source File: utils.py From AnomalyDetectionTransformations with MIT License | 5 votes |
|
def load_cifar100(label_mode='coarse'):
(X_train, y_train), (X_test, y_test) = cifar100.load_data(label_mode=label_mode)
X_train = normalize_minus1_1(cast_to_floatx(X_train))
X_test = normalize_minus1_1(cast_to_floatx(X_test))
return (X_train, y_train), (X_test, y_test)
Example #17
| Source File: PhasedLSTM.py From PhasedLSTM-Keras with MIT License | 5 votes |
|
def get_constants(self, inputs, training=None):
constants = []
if self.implementation == 0 and 0 < self.dropout < 1:
input_shape = K.int_shape(inputs)
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
def dropped_inputs():
return K.dropout(ones, self.dropout)
dp_mask = [K.in_train_phase(dropped_inputs,
ones,
training=training) for _ in range(4)]
constants.append(dp_mask)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(4)])
if 0 < self.recurrent_dropout < 1:
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
def dropped_inputs():
return K.dropout(ones, self.recurrent_dropout)
rec_dp_mask = [K.in_train_phase(dropped_inputs,
ones,
training=training) for _ in range(4)]
constants.append(rec_dp_mask)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(4)])
return constants
Example #18
| Source File: utils.py From AnomalyDetectionTransformations with MIT License | 5 votes |
|
def load_cifar10():
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = normalize_minus1_1(cast_to_floatx(X_train))
X_test = normalize_minus1_1(cast_to_floatx(X_test))
return (X_train, y_train), (X_test, y_test)
Example #19
| Source File: utils.py From AnomalyDetectionTransformations with MIT License | 5 votes |
|
def load_mnist():
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = normalize_minus1_1(cast_to_floatx(np.pad(X_train, ((0, 0), (2, 2), (2, 2)), 'constant')))
X_train = np.expand_dims(X_train, axis=get_channels_axis())
X_test = normalize_minus1_1(cast_to_floatx(np.pad(X_test, ((0, 0), (2, 2), (2, 2)), 'constant')))
X_test = np.expand_dims(X_test, axis=get_channels_axis())
return (X_train, y_train), (X_test, y_test)
Example #20
| Source File: TTRNN.py From TT_RNN with MIT License | 5 votes |
|
def get_constants(self, inputs, training=None):
constants = []
if self.implementation != 0 and 0. < self.dropout < 1:
input_shape = K.int_shape(inputs)
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
def dropped_inputs():
return K.dropout(ones, self.dropout)
dp_mask = [K.in_train_phase(dropped_inputs,
ones,
training=training) for _ in range(4)]
constants.append(dp_mask)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(4)])
if 0. < self.recurrent_dropout < 1:
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
def dropped_inputs():
return K.dropout(ones, self.recurrent_dropout)
rec_dp_mask = [K.in_train_phase(dropped_inputs,
ones,
training=training) for _ in range(4)]
constants.append(rec_dp_mask)
else:
constants.append([K.cast_to_floatx(1.) for _ in range(4)])
return constants
Example #21
| Source File: utils.py From AnomalyDetectionTransformations with MIT License | 5 votes |
|
def load_fashion_mnist():
(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
X_train = normalize_minus1_1(cast_to_floatx(np.pad(X_train, ((0, 0), (2, 2), (2, 2)), 'constant')))
X_train = np.expand_dims(X_train, axis=get_channels_axis())
X_test = normalize_minus1_1(cast_to_floatx(np.pad(X_test, ((0, 0), (2, 2), (2, 2)), 'constant')))
X_test = np.expand_dims(X_test, axis=get_channels_axis())
return (X_train, y_train), (X_test, y_test)
Example #22
| Source File: multiclass_experiment.py From AnomalyDetectionTransformations with MIT License | 5 votes |
|
def load_tinyimagenet(tinyimagenet_path='./'):
images = [plt.imread(fp) for fp in glob(os.path.join(tinyimagenet_path, '*.jpg'))]
for i in range(len(images)):
if len(images[i].shape) != 3:
images[i] = np.stack([images[i], images[i], images[i]], axis=-1)
images = np.stack(images)
images = normalize_minus1_1(K.cast_to_floatx(images))
return images
Example #23
| Source File: utilities.py From NNCF with MIT License | 5 votes |
|
def __init__(self, l1=0., l2=0.):
self.l1 = K.cast_to_floatx(l1)
self.l2 = K.cast_to_floatx(l2)
Example #24
| Source File: ternary_layers.py From nn_playground with MIT License | 5 votes |
|
def get_constants(self, inputs, training=None):
constants = []
if 0 < self.dropout < 1:
input_shape = K.int_shape(inputs)
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, int(input_dim)))
def dropped_inputs():
return K.dropout(ones, self.dropout)
dp_mask = K.in_train_phase(dropped_inputs,
ones,
training=training)
constants.append(dp_mask)
else:
constants.append(K.cast_to_floatx(1.))
if 0 < self.recurrent_dropout < 1:
ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.units))
def dropped_inputs():
return K.dropout(ones, self.recurrent_dropout)
rec_dp_mask = K.in_train_phase(dropped_inputs,
ones,
training=training)
constants.append(rec_dp_mask)
else:
constants.append(K.cast_to_floatx(1.))
return constants
# Aliases
Example #25
| Source File: layers.py From asr-study with MIT License | 5 votes |
|
def get_constants(self, x):
constants = []
for layer in xrange(self.depth):
constant = []
if 0 < self.dropout_U < 1:
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, self.output_dim))
B_U = K.in_train_phase(K.dropout(ones, self.dropout_U), ones)
constant.append(B_U)
else:
constant.append(K.cast_to_floatx(1.))
if layer == 0:
if 0 < self.dropout_W < 1:
input_shape = self.input_spec[0].shape
input_dim = input_shape[-1]
ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
ones = K.tile(ones, (1, input_dim))
B_W = K.in_train_phase(K.dropout(ones,
self.dropout_W), ones)
constant.append(B_W)
else:
constant.append(K.cast_to_floatx(1.))
constants.append(constant)
return constants
Example #26
| Source File: rnnlayer.py From recurrent-attention-for-QA-SQUAD-based-on-keras with MIT License | 5 votes |
|
def get_constants(self, inputs, training=None):
constants = []
constants.append([K.cast_to_floatx(1.) for _ in range(3)])
return constants
Example #27
| Source File: custom.py From DLWP with MIT License | 5 votes |
|
def latitude_weighted_loss(loss_function=mean_squared_error, lats=None, output_shape=(), axis=-2, weighting='cosine'):
"""
Create a loss function that weights inputs by a function of latitude before calculating the loss.
:param loss_function: method: Keras loss function to apply after the weighting
:param lats: ndarray: 1-dimensional array of latitude coordinates
:param output_shape: tuple: shape of expected model output
:param axis: int: latitude axis in model output shape
:param weighting: str: type of weighting to apply. Options are:
cosine: weight by the cosine of the latitude (default)
midlatitude: weight by the cosine of the latitude but also apply a 25% reduction to the equator and boost
to the mid-latitudes
:return: callable loss function
"""
if weighting not in ['cosine', 'midlatitude']:
raise ValueError("'weighting' must be one of 'cosine' or 'midlatitude'")
if lats is not None:
lat_tensor = K.zeros(lats.shape)
lat_tensor.assign(K.cast_to_floatx(lats[:]))
weights = K.cos(lat_tensor * np.pi / 180.)
if weighting == 'midlatitude':
weights = weights + 0.5 * K.pow(K.sin(lat_tensor * 2 * np.pi / 180.), 2.)
weight_shape = output_shape[axis:]
for d in weight_shape[1:]:
weights = K.expand_dims(weights, axis=-1)
weights = K.repeat_elements(weights, d, axis=-1)
else:
weights = K.ones(output_shape)
def lat_loss(y_true, y_pred):
return loss_function(y_true * weights, y_pred * weights)
return lat_loss
Example #28
| Source File: custom.py From DLWP with MIT License | 5 votes |
|
def __init__(self, loss_function, lats, data_format='channels_last', weighting='cosine'):
"""
Initialize a weighted loss.
:param loss_function: method: Keras loss function to apply after the weighting
:param lats: ndarray: 1-dimensional array of latitude coordinates
:param data_format: Keras data_format ('channels_first' or 'channels_last')
:param weighting: str: type of weighting to apply. Options are:
cosine: weight by the cosine of the latitude (default)
midlatitude: weight by the cosine of the latitude but also apply a 25% reduction to the equator and boost
to the mid-latitudes
"""
self.loss_function = loss_function
self.lats = lats
self.data_format = K.normalize_data_format(data_format)
if weighting not in ['cosine', 'midlatitude']:
raise ValueError("'weighting' must be one of 'cosine' or 'midlatitude'")
self.weighting = weighting
lat_tensor = K.zeros(lats.shape)
print(lats)
lat_tensor.assign(K.cast_to_floatx(lats[:]))
self.weights = K.cos(lat_tensor * np.pi / 180.)
if self.weighting == 'midlatitude':
self.weights = self.weights - 0.25 * K.sin(lat_tensor * 2 * np.pi / 180.)
self.is_init = False
self.__name__ = 'latitude_weighted_loss'
Example #29
| Source File: constraints_test.py From keras-contrib with MIT License | 5 votes |
|
def test_clip():
clip_instance = constraints.clip()
clipped = clip_instance(K.variable(example_array))
assert(np.max(np.abs(K.eval(clipped))) <= K.cast_to_floatx(0.01))
clip_instance = constraints.clip(0.1)
clipped = clip_instance(K.variable(example_array))
assert(np.max(np.abs(K.eval(clipped))) <= K.cast_to_floatx(0.1))
Example #30
| Source File: sinerelu.py From keras-contrib with MIT License | 5 votes |
|
def __init__(self, epsilon=0.0025, **kwargs):
super(SineReLU, self).__init__(**kwargs)
self.supports_masking = True
self.epsilon = K.cast_to_floatx(epsilon)
