本文整理汇总了Python中keras.backend.softmax方法的典型用法代码示例。如果您正苦于以下问题:Python backend.softmax方法的具体用法?Python backend.softmax怎么用?Python backend.softmax使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.backend
的用法示例。
在下文中一共展示了backend.softmax方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: step
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def step(self, x, states):
h = states[0]
# states[1] necessary?
# equals K.dot(X, self._W1) + self._b2 with X.shape=[bs, T, input_dim]
total_x_prod = states[-1]
# comes from the constants (equals the input sequence)
X = states[-2]
# expand dims to add the vector which is only valid for this time step
# to total_x_prod which is valid for all time steps
hw = K.expand_dims(K.dot(h, self._W2), 1)
additive_atn = total_x_prod + hw
attention = K.softmax(K.dot(additive_atn, self._V), axis=1)
x_weighted = K.sum(attention * X, [1])
x = K.dot(K.concatenate([x, x_weighted], 1), self._W3) + self._b3
h, new_states = self.layer.cell.call(x, states[:-2])
return h, new_states
示例2: content_addressing
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def content_addressing(memory_t, key_vector_t, key_strength_t):
'''
Focusing by content.
:param memory_t: external memory.
:param key_vector_t: key vector.
:param key_strength_t: the strength of key.
:return:
'''
# print("content addressing:")
# print(">>memory_t")
# print(key_vector_t)
# print(">>key_vector_t")
# print(key_vector_t)
# print(">>key_strength_t")
# print(key_strength_t)
_weight_content_t = \
key_strength_t * cosine_similarity_group(key_vector_t, memory_t)
weight_content_t = softmax(_weight_content_t)
# print("_weight_content_t")
# print(_weight_content_t)
return weight_content_t
示例3: labelembed_loss
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def labelembed_loss(out1, out2, tar, targets, tau = 2., alpha = 0.9, beta = 0.5, num_classes = 100):
out2_prob = K.softmax(out2)
tau2_prob = K.stop_gradient(K.softmax(out2 / tau))
soft_tar = K.stop_gradient(K.softmax(tar))
L_o1_y = K.sparse_categorical_crossentropy(output = K.softmax(out1), target = targets)
pred = K.argmax(out2, axis = -1)
mask = K.stop_gradient(K.cast(K.equal(pred, K.cast(targets, 'int64')), K.floatx()))
L_o1_emb = -cross_entropy(out1, soft_tar) # pylint: disable=invalid-unary-operand-type
L_o2_y = K.sparse_categorical_crossentropy(output = out2_prob, target = targets)
L_emb_o2 = -cross_entropy(tar, tau2_prob) * mask * (K.cast(K.shape(mask)[0], K.floatx())/(K.sum(mask)+1e-8)) # pylint: disable=invalid-unary-operand-type
L_re = K.relu(K.sum(out2_prob * K.one_hot(K.cast(targets, 'int64'), num_classes), axis = -1) - alpha)
return beta * L_o1_y + (1-beta) * L_o1_emb + L_o2_y + L_emb_o2 + L_re
示例4: _softmax
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def _softmax(x, axis=-1, alpha=1):
"""
building on keras implementation, allow alpha parameter
Softmax activation function.
# Arguments
x : Tensor.
axis: Integer, axis along which the softmax normalization is applied.
alpha: a value to multiply all x
# Returns
Tensor, output of softmax transformation.
# Raises
ValueError: In case `dim(x) == 1`.
"""
x = alpha * x
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim > 2:
e = K.exp(x - K.max(x, axis=axis, keepdims=True))
s = K.sum(e, axis=axis, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor that is 1D')
示例5: mlp_v2
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def mlp_v2():
model = Sequential()
model.add(Dense(2048, input_shape=(21099,)))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
# model.add(Dense(1024))
# model.add(Activation('relu'))
# model.add(Dropout(0.5))
# model.add(BatchNormalization())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(6))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='nadam',
metrics=['accuracy'])
return model
示例6: call
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def call(self, u_vecs):
if self.share_weights:
u_hat_vecs = K.conv1d(u_vecs, self.W)
else:
u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])
batch_size = K.shape(u_vecs)[0]
input_num_capsule = K.shape(u_vecs)[1]
u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
b = K.zeros_like(u_hat_vecs[:, :, :, 0]) # shape = [None, num_capsule, input_num_capsule]
for i in range(self.routings):
b = K.permute_dimensions(b, (0, 2, 1)) # shape = [None, input_num_capsule, num_capsule]
c = K.softmax(b)
c = K.permute_dimensions(c, (0, 2, 1))
b = K.permute_dimensions(b, (0, 2, 1))
outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
if i < self.routings - 1:
b = K.batch_dot(outputs, u_hat_vecs, [2, 3])
return outputs
示例7: CapsuleNet
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def CapsuleNet(n_capsule = 10, n_routings = 5, capsule_dim = 16,
n_recurrent=100, dropout_rate=0.2, l2_penalty=0.0001):
K.clear_session()
inputs = Input(shape=(170,))
x = Embedding(21099, 300, trainable=True)(inputs)
x = SpatialDropout1D(dropout_rate)(x)
x = Bidirectional(
CuDNNGRU(n_recurrent, return_sequences=True,
kernel_regularizer=l2(l2_penalty),
recurrent_regularizer=l2(l2_penalty)))(x)
x = PReLU()(x)
x = Capsule(
num_capsule=n_capsule, dim_capsule=capsule_dim,
routings=n_routings, share_weights=True)(x)
x = Flatten(name = 'concatenate')(x)
x = Dropout(dropout_rate)(x)
# fc = Dense(128, activation='sigmoid')(x)
outputs = Dense(6, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy'])
return model
示例8: call
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def call(self, u_vecs):
if self.share_weights:
u_hat_vecs = K.conv1d(u_vecs, self.W)
else:
u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])
batch_size = K.shape(u_vecs)[0]
input_num_capsule = K.shape(u_vecs)[1]
u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
# final u_hat_vecs.shape = [None, num_capsule, input_num_capsule, dim_capsule]
b = K.zeros_like(u_hat_vecs[:, :, :, 0]) # shape = [None, num_capsule, input_num_capsule]
outputs = None
for i in range(self.routings):
b = K.permute_dimensions(b, (0, 2, 1)) # shape = [None, input_num_capsule, num_capsule]
c = K.softmax(b)
c = K.permute_dimensions(c, (0, 2, 1))
b = K.permute_dimensions(b, (0, 2, 1))
outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
if i < self.routings - 1:
b = K.batch_dot(outputs, u_hat_vecs, [2, 3])
return outputs
示例9: mask_attention_if_needed
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def mask_attention_if_needed(self, dot_product):
"""
Makes sure that (when enabled) each position
(of a decoder's self-attention) cannot attend to subsequent positions.
This is achieved by assigning -inf (or some large negative number)
to all invalid connections. Later softmax will turn them into zeros.
We need this to guarantee that decoder's predictions are based
on what has happened before the position, not after.
The method does nothing if masking is turned off.
:param dot_product: scaled dot-product of Q and K after reshaping them
to 3D tensors (batch * num_heads, rows, cols)
"""
if not self.use_masking:
return dot_product
last_dims = K.int_shape(dot_product)[-2:]
low_triangle_ones = (
np.tril(np.ones(last_dims))
# to ensure proper broadcasting
.reshape((1,) + last_dims))
inverse_low_triangle = 1 - low_triangle_ones
close_to_negative_inf = -1e9
result = (
K.constant(low_triangle_ones, dtype=K.floatx()) * dot_product +
K.constant(close_to_negative_inf * inverse_low_triangle))
return result
示例10: softmax
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def softmax(x, axis=1):
"""Softmax activation function.
# Arguments
x : Tensor.
axis: Integer, axis along which the softmax normalization is applied.
# Returns
Tensor, output of softmax transformation.
# Raises
ValueError: In case `dim(x) == 1`.
"""
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim > 2:
e = K.exp(x - K.max(x, axis=axis, keepdims=True))
s = K.sum(e, axis=axis, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor that is 1D')
示例11: _get_weight_vector
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def _get_weight_vector(self, M, w_tm1, k, beta, g, s, gamma):
# M = tf.Print(M, [M, w_tm1, k], message='get weights beg1: ')
# M = tf.Print(M, [beta, g, s, gamma], message='get weights beg2: ')
# Content adressing, see Chapter 3.3.1:
num = beta * _cosine_distance(M, k)
w_c = K.softmax(num) # It turns out that equation (5) is just softmax.
# Location adressing, see Chapter 3.3.2:
# Equation 7:
w_g = (g * w_c) + (1-g)*w_tm1
# C_s is the circular convolution
#C_w = K.sum((self.C[None, :, :, :] * w_g[:, None, None, :]),axis=3)
# Equation 8:
# TODO: Explain
C_s = K.sum(K.repeat_elements(self.C[None, :, :, :], self.batch_size, axis=0) * s[:,:,None,None], axis=1)
w_tilda = K.batch_dot(C_s, w_g)
# Equation 9:
w_out = _renorm(w_tilda ** gamma)
return w_out
示例12: softmax_activation
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def softmax_activation(self, mem):
"""Softmax activation."""
# spiking_samples = k.less_equal(k.random_uniform([self.config.getint(
# 'simulation', 'batch_size'), 1]), 300 * self.dt / 1000.)
# spiking_neurons = k.T.repeat(spiking_samples, 10, axis=1)
# activ = k.T.nnet.softmax(mem)
# max_activ = k.max(activ, axis=1, keepdims=True)
# output_spikes = k.equal(activ, max_activ).astype(k.floatx())
# output_spikes = k.T.set_subtensor(output_spikes[k.equal(
# spiking_neurons, 0).nonzero()], 0.)
# new_and_reset_mem = k.T.set_subtensor(mem[spiking_neurons.nonzero()],
# 0.)
# self.add_update([(self.mem, new_and_reset_mem)])
# return output_spikes
return k.T.mul(k.less_equal(k.random_uniform(mem.shape),
k.softmax(mem)), self.v_thresh)
示例13: set_reset_mem
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def set_reset_mem(self, mem, spikes):
"""
Reset membrane potential ``mem`` array where ``spikes`` array is
nonzero.
"""
spike_idxs = k.T.nonzero(spikes)
if (hasattr(self, 'activation_str') and
self.activation_str == 'softmax'):
new = mem.copy() # k.T.set_subtensor(mem[spike_idxs], 0.)
elif self.config.get('cell', 'reset') == 'Reset by subtraction':
if self.payloads: # Experimental.
new = k.T.set_subtensor(mem[spike_idxs], 0.)
else:
pos_spike_idxs = k.T.nonzero(k.greater(spikes, 0))
neg_spike_idxs = k.T.nonzero(k.less(spikes, 0))
new = k.T.inc_subtensor(mem[pos_spike_idxs], -self.v_thresh)
new = k.T.inc_subtensor(new[neg_spike_idxs], self.v_thresh)
elif self.config.get('cell', 'reset') == 'Reset by modulo':
new = k.T.set_subtensor(mem[spike_idxs],
mem[spike_idxs] % self.v_thresh)
else: # self.config.get('cell', 'reset') == 'Reset to zero':
new = k.T.set_subtensor(mem[spike_idxs], 0.)
self.add_update([(self.mem, new)])
示例14: call
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def call(self, u_vecs):
if self.share_weights:
u_hat_vecs = K.conv1d(u_vecs, self.W)
else:
u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])
batch_size = K.shape(u_vecs)[0]
input_num_capsule = K.shape(u_vecs)[1]
u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule)) # noqa
u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
# final u_hat_vecs.shape = [None, num_capsule, input_num_capsule, dim_capsule] # noqa
b = K.zeros_like(u_hat_vecs[:, :, :, 0]) # shape = [None, num_capsule, input_num_capsule] # noqa
for i in range(self.routings):
b = K.permute_dimensions(b, (0, 2, 1)) # shape = [None, input_num_capsule, num_capsule] # noqa
c = K.softmax(b)
c = K.permute_dimensions(c, (0, 2, 1))
b = K.permute_dimensions(b, (0, 2, 1))
outputs = self.activation(tf.keras.backend.batch_dot(c, u_hat_vecs, [2, 2])) # noqa
if i < self.routings - 1:
b = tf.keras.backend.batch_dot(outputs, u_hat_vecs, [2, 3])
return outputs
示例15: call
# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import softmax [as 别名]
def call(self, x, mask=None):
uit = K.tanh(K.dot(x, self.Ws1))
ait = K.dot(uit, self.Ws2)
ait = K.permute_dimensions(ait, (0, 2, 1))
A = K.softmax(ait, axis=1)
M = K.batch_dot(A, x)
if self.punish:
A_T = K.permute_dimensions(A, (0, 2, 1))
tile_eye = K.tile(K.eye(self.weight_ws2), [self.batch_size, 1])
tile_eye = K.reshape(
tile_eye, shape=[-1, self.weight_ws2, self.weight_ws2])
AA_T = K.batch_dot(A, A_T) - tile_eye
P = K.l2_normalize(AA_T, axis=(1, 2))
return M, P
else:
return M