def time_distributed_dense(x, w, b=None, dropout=None,
input_dim=None, output_dim=None, timesteps=None, activation='linear'):
'''Apply y.w + b for every temporal slice y of x.
'''
activation = activations.get(activation)
if not input_dim:
# won't work with TensorFlow
input_dim = K.shape(x)[2]
if not timesteps:
# won't work with TensorFlow
timesteps = K.shape(x)[1]
if not output_dim:
# won't work with TensorFlow
output_dim = K.shape(w)[1]
if dropout is not None and 0. < dropout < 1.:
# apply the same dropout pattern at every timestep
ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = K.dropout(ones, dropout)
expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
x = K.in_train_phase(x * expanded_dropout_matrix, x)
# collapse time dimension and batch dimension together
x = K.reshape(x, (-1, input_dim))
x = K.dot(x, w)
if b:
x = x + b
# reshape to 3D tensor
x = K.reshape(activation(x), (-1, timesteps, output_dim))
return x
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
def dot_product_attention(self, x, seq_len=None, dropout=0.1, training=None):
q, k, v = x
logits = tf.matmul(q, k, transpose_b=True)
if self.bias:
logits += self.b
if seq_len is not None:
logits = self.mask_logits(logits, seq_len)
weights = tf.nn.softmax(logits, name="attention_weights")
weights = K.in_train_phase(K.dropout(weights, dropout), weights, training=training)
x = tf.matmul(weights, v)
return x
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
def call(self, inputs, **kwargs):
main_input, embedding_matrix = inputs
input_shape_tensor = K.shape(main_input)
last_input_dim = K.int_shape(main_input)[-1]
emb_input_dim, emb_output_dim = K.int_shape(embedding_matrix)
projected = K.dot(K.reshape(main_input, (-1, last_input_dim)), self.projection)
if self.add_biases:
projected = K.bias_add(projected, self.biases, data_format='channels_last')
if 0 < self.projection_dropout < 1:
projected = K.in_train_phase( lambda: K.dropout(projected, self.projection_dropout), projected, training=kwargs.get('training'))
attention = K.dot(projected, K.transpose(embedding_matrix))
if self.scaled_attention:
sqrt_d = K.constant(math.sqrt(emb_output_dim), dtype=K.floatx())
attention = attention / sqrt_d
result = K.reshape( self.activation(attention), (input_shape_tensor[0], input_shape_tensor[1], emb_input_dim))
return result
def call(self, inputs, **kwargs):
feature_num = self.feature_num
[v, q] = inputs
v_proj = K.tanh(K.dot(v, self.v_proj))
q_proj = K.tanh(K.dot(q, self.q_proj))
q_proj = K.expand_dims(q_proj, 1)
q_proj = tf.tile(q_proj, [1, feature_num, 1])
joint_repr = v_proj * q_proj
joint_repr = K.dropout(joint_repr, self.drop_rate)
logit = K.dot(joint_repr, self.linear)
logit = K.reshape(logit, shape=[-1, feature_num])
logit = K.softmax(K.l2_normalize(logit)) #[batch,K]
logit = K.expand_dims(logit, -1)
self.result = K.sum(logit * v, 1) #v:[batch,K,4096]
return self.result
def _time_distributed_dense(x,
w,
b=None,
dropout=None,
input_dim=None,
output_dim=None,
timesteps=None,
training=None):
"""Apply `y . w + b` for every temporal slice y of x.
# Arguments
x: input tensor.
w: weight matrix.
b: optional bias vector.
dropout: wether to apply dropout (same dropout mask
for every temporal slice of the input).
input_dim: integer; optional dimensionality of the input.
output_dim: integer; optional dimensionality of the output.
timesteps: integer; optional number of timesteps.
training: training phase tensor or boolean.
# Returns
Output tensor.
"""
if not input_dim:
input_dim = K.shape(x)[2]
if not timesteps:
timesteps = K.shape(x)[1]
if not output_dim:
output_dim = K.shape(w)[1]
if dropout is not None and 0. < dropout < 1.:
# apply the same dropout pattern at every timestep
ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = K.dropout(ones, dropout)
expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)
# collapse time dimension and batch dimension together
x = K.reshape(x, (-1, input_dim))
x = K.dot(x, w)
if b is not None:
x = K.bias_add(x, b)
# reshape to 3D tensor
if K.backend() == 'tensorflow':
x = K.reshape(x, K.stack([-1, timesteps, output_dim]))
x.set_shape([None, None, output_dim])
else:
x = K.reshape(x, (-1, timesteps, output_dim))
return x
def call(self, inputs, train=True, cache=None):
assert isinstance(inputs, (list, tuple)) and len(inputs) == 3
x = inputs[0]
y = inputs[1]
bias = inputs[2]
q = self.q_dense_layer(x)
k = self.k_dense_layer(y)
v = self.v_dense_layer(y)
if cache is not None:
# Combine cached keys and values with new keys and values.
k = K.concatenate([cache["k"], k], axis=1)
v = K.concatenate([cache["v"], v], axis=1)
# Update cache
cache["k"] = k
cache["v"] = v
# [batch_size, seq_len, hidden_size]
# Split q, k, v into heads.
q = self.split_heads(q)
k = self.split_heads(k)
v = self.split_heads(v)
# Scale q to prevent the dot product between q and k from growing too large.
depth = (self.hidden_size // self.num_heads)
q *= depth ** -0.5
# Calculate dot product attention
logits = K2.matmul(q, K.permute_dimensions(k, (0, 1, 3, 2)))
logits += bias
weights = K.softmax(logits, axis=-1)
if train:
weights = K.dropout(weights, self.attention_dropout)
attention_output = K2.matmul(weights, v)
# Recombine heads --> [batch_size, length, hidden_size]
attention_output = self.combine_heads(attention_output)
# Run the combined outputs through another linear projection layer.
attention_output = self.output_dense_layer(attention_output)
return attention_output
def train(num_classes=100, epochs=100, reps=1):
(x_train, y_train) = load_data(num_classes,reps)
model = Sequential()
model.add(Lambda(lambda x: K.dropout(x, level=0.9), input_shape=input_shape))#permanent dropout
model.add(Conv2D(32, kernel_size=(3, 3), kernel_initializer='uniform'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), kernel_initializer='uniform'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, kernel_initializer='uniform'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(num_classes, activation='softmax'))
# model.load_weights("saved_bn.hdf5")
model.summary()
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True),
metrics=['accuracy'])
#add callbacks
tensorboard = TensorBoard(log_dir="logs/{}".format(time()), histogram_freq=10, write_graph=True, write_images=True)
checkpoint = ModelCheckpoint("saved_bn.hdf5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
stopper = EarlyStopping(monitor='val_acc', min_delta=0.01, patience=10, verbose=1, mode='auto')
model.fit(x_train, y_train,
batch_size=100,
epochs=epochs,
verbose=1,
shuffle=True,
validation_data=(x_train,y_train),
callbacks=[tensorboard, checkpoint, stopper])
return model
def time_distributed_dense(x, w, b=None, dropout=None,
input_dim=None, output_dim=None,
timesteps=None, training=None):
"""Apply `y . w + b` for every temporal slice y of x.
# Arguments
x: input tensor.
w: weight matrix.
b: optional bias vector.
dropout: wether to apply dropout (same dropout mask
for every temporal slice of the input).
input_dim: integer; optional dimensionality of the input.
output_dim: integer; optional dimensionality of the output.
timesteps: integer; optional number of timesteps.
training: training phase tensor or boolean.
# Returns
Output tensor.
"""
if not input_dim:
input_dim = K.shape(x)[2]
if not timesteps:
timesteps = K.shape(x)[1]
if not output_dim:
output_dim = K.shape(w)[1]
if dropout is not None and 0. < dropout < 1.:
# apply the same dropout pattern at every timestep
ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = K.dropout(ones, dropout)
expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)
# collapse time dimension and batch dimension together
x = K.reshape(x, (-1, input_dim))
x = K.dot(x, w)
if b is not None:
x = K.bias_add(x, b)
# reshape to 3D tensor
if K.backend() == 'tensorflow':
x = K.reshape(x, K.stack([-1, timesteps, output_dim]))
x.set_shape([None, None, output_dim])
else:
x = K.reshape(x, (-1, timesteps, output_dim))
return x
def step_do(self, step_in, states): # 定义每一步的迭代
in_value = step_in
if 0 < self.dropout < 1.:
self._dropout_mask = K.in_train_phase(
K.dropout(K.ones_like(step_in), self.dropout),
K.ones_like(step_in))
if 0 < self.dropout < 1.:
in_value = step_in * self._dropout_mask
'''
g1 = states[0][:, :-18]
g2 = states[1][:, :-18]
g3 = in_value[:, :-18]
d1 = K.sigmoid(K.sqrt(K.sum((K.square(g3-g1)),axis=-1,keepdims=True)))
d2 = K.sigmoid(K.sqrt(K.sum((K.square(g3-g2)),axis=-1,keepdims=True)))
d1 = K.sigmoid(K.sum((K.abs(in_value-states[0])/self.units),axis=-1,keepdims=True))
d2 = K.sigmoid(K.sum((K.abs(in_value-states[1])/self.units),axis=-1,keepdims=True))
'''
d1 = K.sigmoid(states[0] * in_value) / 2
d2 = K.sigmoid(states[1] * in_value) / 2
#update=1#K.sigmoid(K.dot(states[0], self.state_kernel)+ K.dot(step_in, self.input_kernel))
''''''
#print('d1.shape',d1.shape)
state1 = d1 * states[0] + (1 - d1) * in_value
print('state1.shape', state1.shape)
state2 = (1 - d2) * states[1] + d2 * in_value
#outputs = (1-update)*states[0]+update*step_in
'''
lt = K.expand_dims(state1,axis=-2)
st = K.expand_dims(state2,axis=-2)
outputs = K.concatenate([lt, st], axis=-2)
out1 = K.dot(state1, self.encode_kernel)
out2 = K.dot(state2, self.encode_kernel)
'''
outputs = K.concatenate([state1, state2], axis=-1)
#outputs = K.relu(outputs)
return outputs, [state1, state2]
def _time_distributed_dense(x,
w,
b=None,
dropout=None,
input_dim=None,
output_dim=None,
timesteps=None,
training=None):
"""Apply `y . w + b` for every temporal slice y of x.
# Arguments
x: input tensor.
w: weight matrix.
b: optional bias vector.
dropout: wether to apply dropout (same dropout mask
for every temporal slice of the input).
input_dim: integer; optional dimensionality of the input.
output_dim: integer; optional dimensionality of the output.
timesteps: integer; optional number of timesteps.
training: training phase tensor or boolean.
# Returns
Output tensor.
"""
if not input_dim:
input_dim = K.shape(x)[2]
if not timesteps:
timesteps = K.shape(x)[1]
if not output_dim:
output_dim = K.shape(w)[1]
if dropout is not None and 0. < dropout < 1.:
# apply the same dropout pattern at every timestep
ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = K.dropout(ones, dropout)
expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)
# maybe below is more clear implementation compared to older keras
# at least it works the same for tensorflow, but not tested on other backends
x = K.dot(x, w)
if b is not None:
x = K.bias_add(x, b)
return x
def _call_attention(self, Key, Value, Query):
r"""self-attention就是通过相似度函数计算得的相似矩阵过softmax后与自身点乘得到
.. math:: A = Softmax(Similarity(Source,Query))
.. math:: C = A \cdot Source
"""
if isinstance(self.similarity, Callable):
sim = self.similarity(Key, Query)
else:
sim = getattr(self, self.similarity)(Key, Query)
sm = activations.softmax(sim)
if self.dropout_rate:
sm = K.dropout(sm, self.dropout_rate)
if isinstance(self.mergfunc, Callable):
result = self.mergfunc(sm, Value)
elif isinstance(self.mergfunc, str):
result = getattr(self, self.mergfunc, 'batch_dot_merg')(sm, Value)
else:
result = getattr(self, 'batch_dot_merg')(sm, Value)
return result
def deconv2d(layer_input,
filters,
f_size=8,
dropout_rate=0,
permanent=False):
"""Layers used during upsampling"""
u = UpSampling2D(size=2)(layer_input)
u = Conv2D(filters,
kernel_size=f_size,
strides=1,
padding='same',
activation='relu')(u)
if dropout_rate and not permanent:
u = Dropout(dropout_rate)(u)
elif dropout_rate and permanent:
# permanent droput from my main man fchollet <3
u = Lambda(lambda x: K.dropout(x, level=dropout_rate))(u)
u = BatchNormalization(momentum=0.8)(u)
return u
def call(self, x, mask=None):
if mask is None:
mask = T.mean(T.ones_like(x), axis=-1)
mask = T.cast(mask, T.floatx())
dr_perc = 0.5
mask1 = T.dropout(mask, level=dr_perc)
mask1 = T.clip(mask1, 0, 1)
mod_smax = T.max(x[:, :, 0] * mask1, axis=1).dimshuffle(0, 'x')
smax = T.max(x[:, :, 0] * mask,
axis=1).dimshuffle(0, 'x') #(nb_samples, np_features)
smin = T.min(x[:, :, 0] * mask,
axis=1).dimshuffle(0, 'x') #(nb_samples, np_features)
# mod_smax=T.expand_dims(T.max(x[:,:,0]*mask1,axis=1), 1)
# smax = T.expand_dims(T.max(x[:,:,0]*mask,axis=1), 1) #(nb_samples, np_features)
# smin = T.expand_dims(T.min(x[:,:,0]*mask,axis=1), 1) #(nb_samples, np_features)
x_rounded = x[:, :, 0] * mask
sum_unmasked = T.batch_dot(x_rounded, mask,
axes=1) # (nb_samples,np_features)
ssum = T.sum(x, axis=-2) #(nb_samples, np_features)
rcnt = T.sum(
mask, axis=-1, keepdims=True
) #(nb_samples) # number of unmasked samples in each record
bag_label = sum_unmasked / rcnt
smean = ssum / rcnt
# # sigmoid weighted mean:
# weight_fn=T.reshape(T.transpose(T.tile(T.reshape(T.variable(get_weight_fn(100)),(100,1)),T.shape(x)[0])),(T.shape(x)[0],T.shape(x)[1],1))
# weighted_x=weight_fn*x
# wsum=T.sum(weighted_x,axis=-2) #(nb_samples, np_features)
## weight_sum=T.reshape(T.batch_dot(T.ones_like(x),weight_fn,axes=1),T.shape(rcnt)) # used T.ones_like(x) instead of x to check if I am seeing the outputs..which helped me debug
# wmean=wsum # because the weights are normalized
# sofmax=(1/largeNum)*T.log(T.sum(T.exp()))
# return bag_label
return smax # max voting
def call(self, inputs, mask=None):
assert len(inputs) == 2
query = K.bias_add(K.dot(inputs[0], self.W_query), self.bias_query)
if self.query_act is not None:
query = self.query_act(query)
key = K.bias_add(K.dot(inputs[1], self.W_key), self.bias_key)
if self.key_act is not None:
key = self.key_act(key)
value = K.bias_add(K.dot(inputs[1], self.W_value), self.bias_value)
if self.value_act is not None:
value = self.value_act(value)
query = K.reshape(query, shape=(-1, K.int_shape(inputs[0])[1], self.num_attention_heads, self.size_per_head))
query = K.permute_dimensions(query, pattern=(0,2,1,3))
key = K.reshape(key, shape=(-1, K.int_shape(inputs[1])[1], self.num_attention_heads, self.size_per_head))
key = K.permute_dimensions(key, pattern=(0,2,1,3))
value = K.reshape(value, shape=(-1, K.int_shape(inputs[1])[1], self.num_attention_heads, self.size_per_head))
value = K.permute_dimensions(value, pattern=(0,2,1,3))
attention_scores = K.batch_dot(query, key, axes=(3,3))
attention_scores /= np.sqrt(self.size_per_head)
if mask is not None and mask != [None, None]:
mask_q, mask_k = mask
mask_q = K.cast(mask_q, K.floatx())
mask_k = K.cast(mask_k, K.floatx())
mask_q = K.expand_dims(mask_q)
mask_k = K.expand_dims(mask_k)
attention_mask = K.batch_dot(mask_q, mask_k, axes=(-1,-1))
attention_mask = K.expand_dims(attention_mask, axis=1)
adder = (1 - attention_mask) * (-10000.0)
attention_scores += adder
attention_probs = K.softmax(attention_scores, axis=-1)
attention_probs = K.dropout(attention_probs, self.attention_probs_dropout_prob)
context = K.batch_dot(attention_probs, value, axes=(3,2))
context = K.permute_dimensions(context, pattern=(0,2,1,3))
context = K.reshape(context, shape=(-1, K.int_shape(inputs[0])[1], self.num_attention_heads*self.size_per_head))
return context
def time_distributed_dense(input_tensor,
weight,
bias=None,
timesteps=None,
input_dim=None,
output_dim=None,
dropout=None,
training=None):
"""Apply t.weight + bias for every t of timesteps of input
input_tensor: input tensor shape = (batch num, timestep, input_dim)
weight: weight tensor = (input_dim, output_dim)
bias: optional bias
dropout: dropout value
training: training phase boolean
"""
if timesteps is None:
timesteps = K.shape(input_tensor)[1]
if input_dim is None:
input_dim = K.shape(input_tensor)[2]
if output_dim is None:
output_dim = K.shape(weight)[1]
if dropout is not None and 0. < dropout < 1.:
#apply dropout at every timestep
ones = K.ones_like(K.reshape(input_tensor[:, 0, :], (-1, input_dim)))
dropout_tensor = K.dropout(ones, dropout)
dropout_tensor_with_timestep = K.repeat(dropout_tensor, timesteps)
input_tensor = K.in_train_phase(input_tensor *
dropout_tensor_with_timestep,
input_tensor,
training=training)
#collapse timestep and batch num together
input_tensor = K.reshape(input_tensor, (-1, input_dim))
input_tensor = K.dot(input_tensor, weight)
if bias is not None:
input_tensor = K.bias_add(input_tensor, bias)
output_tensor = K.reshape(input_tensor, (-1, timesteps, output_dim))
return output_tensor
def _time_distributed_dense(x,
w,
b=None,
dropout=None,
input_dim=None,
output_dim=None,
timesteps=None,
training=None):
"""Apply `y . w + b` for every temporal slice y of x.
# Returns
Output tensor.
"""
if not input_dim:
input_dim = K.shape(x)[2]
if not timesteps:
timesteps = K.shape(x)[1]
if not output_dim:
output_dim = K.shape(w)[1]
if dropout is not None and 0. < dropout < 1.:
# apply the same dropout pattern at every timestep
ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = K.dropout(ones, dropout)
expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)
# collapse time dimension and batch dimension together
x = K.reshape(x, (-1, input_dim))
x = K.dot(x, w)
if b is not None:
x = K.bias_add(x, b)
# reshape to 3D tensor
if K.backend() == 'tensorflow':
x = K.reshape(x, K.stack([-1, timesteps, output_dim]))
x.set_shape([None, None, output_dim])
else:
x = K.reshape(x, (-1, timesteps, output_dim))
return x
def build_model(loss="mse", num_outputs=1):
model = Sequential()
model.add(
ConvLSTM2D(filters=100,
kernel_size=(3, 3),
input_shape=(None, nx, ny, 1),
padding='same',
return_sequences=False))
model.add(BatchNormalization())
model.add(Lambda(lambda x: K.dropout(x, level=0.75)))
model.add(
Dense(
units=num_outputs,
kernel_regularizer=regularizers.l2(0.0001),
))
model.add(Activation("linear"))
model.compile(loss=loss, optimizer='nadam')
return model
def sample_h_given_x(self, x):
"""
Draw sample from p(h|x).
For Bernoulli RBM the conditional probability distribution can be derived to be
p(h_j=1|x) = sigmoid(x^T W[:,j] + bh_j).
"""
h_pre = K.dot(x, self.W) + self.bh # pre-sigmoid (used in cross-entropy error calculation for better numerical stability)
#h_sigm = K.sigmoid(h_pre) # mean of Bernoulli distribution ('p', prob. of variable taking value 1), sometimes called mean-field value
h_sigm = self.activation(self.scaling_h_given_x * h_pre)
# drop out noise
if(0.0 < self.p < 1.0):
noise_shape = self._get_noise_shape(h_sigm)
h_sigm = K.in_train_phase(K.dropout(h_sigm, self.p, noise_shape), h_sigm)
h_samp = random_binomial(shape=h_sigm.shape, n=1, p=h_sigm)
# random sample
# \hat{h} = 1, if p(h=1|x) > uniform(0, 1)
# 0, otherwise
return h_samp, h_pre, h_sigm
def time_distributed_dense(self, x, w, b=None, dropout=None,
input_dim=None, output_dim=None, timesteps=None):
'''Apply y.w + b for every temporal slice y of x.
'''
self.x = x
self.w = w
self.b = b
self.droput = dropout
self.input_dim = input_dim
self.output_dim =output_dim
self.timesteps = timesteps
if not input_dim:
# won't work with TensorFlow
input_dim = K.shape(x)[2]
if not timesteps:
# won't work with TensorFlow
timesteps = K.shape(x)[1]
if not output_dim:
# won't work with TensorFlow
output_dim = K.shape(w)[1]
if dropout:
# apply the same dropout pattern at every timestep
ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = K.dropout(ones, dropout)
expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
x *= expanded_dropout_matrix
# collapse time dimension and batch dimension together
x = K.reshape(x, (-1, input_dim))
x = K.dot(x, w)
if b:
x = x + b
# reshape to 3D tensor
x = K.reshape(x, (-1, timesteps, output_dim))
return x
def time_distributed_dense(x,
w,
b=None,
dropout=None,
input_dim=None,
output_dim=None,
timesteps=None,
activation='linear'):
'''Apply y.w + b for every temporal slice y of x.
'''
activation = activations.get(activation)
if not input_dim:
# won't work with TensorFlow
input_dim = K.shape(x)[2]
if not timesteps:
# won't work with TensorFlow
timesteps = K.shape(x)[1]
if not output_dim:
# won't work with TensorFlow
output_dim = K.shape(w)[1]
if dropout is not None and 0. < dropout < 1.:
# apply the same dropout pattern at every timestep
ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = K.dropout(ones, dropout)
expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
x = K.in_train_phase(x * expanded_dropout_matrix, x)
# collapse time dimension and batch dimension together
x = K.reshape(x, (-1, input_dim))
x = K.dot(x, w)
if b:
x = x + b
# reshape to 3D tensor
x = K.reshape(activation(x), (-1, timesteps, output_dim))
return x
def call(self, x, mask=None):
q = K.dot(x, self.W_q)
k = K.dot(x, self.W_k)
v = K.dot(x, self.W_v)
k_t = K.permute_dimensions(k, (0, 2, 1))
q *= self.depth**(-0.5) # scaled dot-product
logit = K.batch_dot(q, k_t) # [batch_size, q_length, k_length]
# softmax を取ることで正規化します
attention_weight = K.softmax(logit)
# dropout
attention_weight = K.dropout(attention_weight, level=self.dropout_rate)
# 重みに従って value から情報を引いてきます
# [batch_size, q_length, depth]
attention_output = K.batch_dot(attention_weight, v)
output = K.dot(attention_output, self.W_o)
return output
def get_constants(self, x): # needs further edition
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
def step_do(self, step_in, states): # 定义每一步的迭代
in_value = step_in
if 0 < self.dropout < 1.:
self._dropout_mask = K.in_train_phase(
K.dropout(K.ones_like(step_in), self.dropout),
K.ones_like(step_in))
if 0 < self.dropout < 1.:
in_value = step_in * self._dropout_mask
d1 = K.sigmoid(
K.sqrt(
K.sum((K.square(in_value - states[0]) / self.units),
axis=-1,
keepdims=True) / 2))
d2 = K.sigmoid(
K.sqrt(
K.sum((K.square(in_value - states[1]) / self.units),
axis=-1,
keepdims=True) / 2))
'''
d1 = K.sigmoid(K.sum((K.abs(in_value-states[0])/self.units),axis=-1,keepdims=True))
d2 = K.sigmoid(K.sum((K.abs(in_value-states[1])/self.units),axis=-1,keepdims=True))
'''
print('d1.shape', d1.shape)
state1 = d1 * states[0] + (1 - d1) * in_value
print('state1.shape', state1.shape)
state2 = (1 - d2) * states[0] + d2 * in_value
'''
lt = K.expand_dims(state1,axis=-2)
st = K.expand_dims(state2,axis=-2)
outputs = K.concatenate([lt, st], axis=-2)
'''
outputs = K.concatenate([state1, state2], axis=-1)
return outputs, [state1, state2]
def call(self, inputs, **kwargs):
main_input, embedding_matrix = inputs
input_shape_tensor = K.shape(main_input)
last_input_dim = input_shape_tensor[-1]
print('input_shape_tensor: ', input_shape_tensor)
embedding_matrix_shape = K.shape(embedding_matrix)
# vocab_size, hidden_size
emb_input_dim, emb_output_dim = embedding_matrix_shape[
-2], embedding_matrix_shape[-1]
# shape: (main_input_shape[0], hidden_size)
projected = K.dot(K.reshape(main_input, (-1, last_input_dim)),
self.projection)
if self.add_biases:
projected = K.bias_add(projected,
self.biases,
data_format='channels_last')
if 0 < self.projection_dropout < 1:
projected = K.in_train_phase(
lambda: K.dropout(projected, self.projection_dropout),
projected,
training=kwargs.get('training'))
# shape: (main_input_shape[0], vocab_size). Calculate with all words in the vocabulary
attention = K.dot(projected, K.transpose(embedding_matrix))
if self.scaled_attention:
# scaled dot-product attention, described in
# "Attention is all you need" (https://arxiv.org/abs/1706.03762)
#sqrt_d = math.sqrt(emb_output_dim)
sqrt_d = K.sqrt(K.cast(emb_output_dim, dtype=K.floatx()))
attention = attention / sqrt_d
result = K.reshape(
self.activation(attention),
(input_shape_tensor[0], input_shape_tensor[1], emb_input_dim))
return result
def dropped_inputs():
return K.dropout(ones, self.recurrent_dropout)
def dropped_inputs():
return K.dropout(ones, self.dropout)
def dropped_inputs():
return K.dropout(inputs,
self.rate,
noise_shape,
seed=self.seed)
def _dropout(x, level, noise_shape=None, seed=None):
x = K.dropout(x, level, noise_shape, seed)
x *= (1. - level) # compensate for the scaling by the dropout
return x
def get_output(self, train=False):
X = self.get_input(train)
if self.p > 0.:
X = K.dropout(X, level=self.p)
return X
def call(self, x, mask=None):
if 0. < self.rate < 1.:
noise_shape = self._get_noise_shape(x)
x = K.dropout(x, self.rate, noise_shape)
return x
# Train Set float_data = shuffel(float_data) #Nrmalisation train_data float_data[:, :-1] = dp.zero_mean_normalization(float_data[:, :-1]) data = float_data[:, :-1] train_data = data[150:] label_data = float_data[150:, -1] #data[:150]=K.get_value(K.dropout(data[:150],0.2,None,None))*0.8 #data[:150,],float_data[:150,-1]=verarbeit_meanimp(data[:150,],float_data[:150,-1]) #validation data # MCAR train_data_voll = K.dropout(train_data, 0.0001, None, None) train_data_voll = K.get_value(train_data_voll) train_data_20_MCAR = K.dropout(train_data, 0.2, None, None) train_data_20_MCAR = K.get_value(train_data_20_MCAR) train_data_20_MCAR *= 0.8 train_data_40_MCAR = K.dropout(train_data, 0.4, None, None) train_data_40_MCAR = K.get_value(train_data_40_MCAR) train_data_40_MCAR *= 0.6 #MAR sort_float_data = sorted(float_data[150:], key=lambda x: x[1]) sort_float_data = np.array(sort_float_data) sort_train_data = sort_float_data[:, :-1] label_data_MAR = sort_float_data[:, -1]
def VGG19(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,isDrop=False, drop_rate=0.3,
**kwargs):
"""Instantiates the VGG19 architecture.
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`.
# Arguments
include_top: whether to include the 3 fully-connected
layers at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor
(i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)`
(with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional block.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional block, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
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=32,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# Block 1
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(img_input)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
if isDrop:
x = Lambda(lambda x: K.dropout(x, level=drop_rate))(x)
# Block 2
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
if isDrop:
x = Lambda(lambda x: K.dropout(x, level=drop_rate))(x)
# Block 3
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
if isDrop:
x = Lambda(lambda x: K.dropout(x, level=drop_rate))(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv4')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
if isDrop:
x = Lambda(lambda x: K.dropout(x, level=drop_rate))(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv4')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
if isDrop:
x = Lambda(lambda x: K.dropout(x, level=drop_rate))(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv4')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
if isDrop:
x = Lambda(lambda x: K.dropout(x, level=drop_rate))(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
x = layers.Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = keras_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = models.Model(inputs, x, name='vgg19')
# Load weights.
if weights == 'imagenet':
if include_top:
weights_path = keras_utils.get_file(
'vgg19_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
file_hash='cbe5617147190e668d6c5d5026f83318')
else:
weights_path = keras_utils.get_file(
'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
file_hash='253f8cb515780f3b799900260a226db6')
model.load_weights(weights_path)
if backend.backend() == 'theano':
keras_utils.convert_all_kernels_in_model(model)
elif weights is not None:
model.load_weights(weights)
return model
def output(self, train=False):
X = self._default_input(train)
if self.p > 0.:
if train:
X = K.dropout(X, level=self.p)
return X
def PermaDropout(rate):
return Lambda(lambda x: K.dropout(x, level=rate))
def dropped_inputs():
return K.dropout(ones, self.recurrent_dropout)
def dropped_inputs():
return K.dropout(inputs, self.rate, noise_shape,
seed=self.seed)
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
return X_train, Y_train, X_test, Y_test
X_train, Y_train, X_test, Y_test = load_mnist()
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
#model.add(Dropout(0.2))
model.add(Lambda(lambda x: K.dropout(x, level=0.2)))
model.add(Dense(512))
model.add(Activation('relu'))
#model.add(Dropout(0.2))
model.add(Lambda(lambda x: K.dropout(x, level=0.2)))
model.add(Dense(10))
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms)
model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
show_accuracy=True, verbose=2,
validation_data=(X_test, Y_test))
def call(self, x, mask=None):
if 0. < self.p < 1.:
x = K.dropout(x, level=self.p)
return x
def func(args):
old, new = args
pred = K.random_uniform([]) < self.dropout
ret = K.switch(pred, old, old + K.dropout(new, self.dropout))
return K.in_train_phase(ret, old + new)
def call(self, x, mask=None):
if 0. < self.p < 1.:
x = K.in_train_phase(K.dropout(x, level=self.p), x)
return x
