Python backend.zeros方法代码示例

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def call(self, inputs, **kwargs):
        # (batch_size, 1, input_num_capsule, input_dim_capsule)
        expand_inputs = K.expand_dims(inputs, axis=1)
        # (batch_size, num_capsule, input_num_capsule, input_dim_capsule)
        expand_inputs = K.tile(expand_inputs, (1, self.num_capsule, 1, 1))
        # (batch_size, num_capsule, input_num_capsule, dim_capsule)
        u_hat = K.map_fn(lambda x: K.batch_dot(x, self.W, axes=[2, 3]), expand_inputs)

        if self.num_routing <= 0:
            self.num_routing = 3
        # (batch_size, num_capsule, input_num_capsule)
        b = K.zeros((K.shape(u_hat)[0], self.num_capsule, self.input_num_capsule))
        for i in xrange(self.num_routing):
            # (batch_size, num_capsule, input_num_capsule)
            c = softmax(b, axis=1)
            # (batch_size, num_capsule, dim_capsule)
            s = K.batch_dot(c, u_hat, axes=[2, 2])
            squashed_s = squash(s)
            if i < self.num_routing - 1:
                # (batch_size, num_capsule, input_num_capsule)
                b += K.batch_dot(squashed_s, u_hat, axes=[2, 3])
        return squashed_s 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def reset_states(self):
		assert self.stateful, 'Layer must be stateful.'
		input_shape = self.input_spec[0].shape

		if not input_shape[0]:
			raise Exception('If a RNN is stateful, a complete ' +
							'input_shape must be provided (including batch size).')

		if hasattr(self, 'states'):
			K.set_value(self.states[0],
			            np.zeros((input_shape[0], self.hidden_recurrent_dim)))
			K.set_value(self.states[1],
			            np.zeros((input_shape[0], self.input_dim)))
			K.set_value(self.states[2],
			            np.zeros((input_shape[0], self.hidden_dim)))
		else:
			self.states = [K.zeros((input_shape[0], self.hidden_recurrent_dim)),
							K.zeros((input_shape[0], self.input_dim)),
							K.zeros((input_shape[0], self.hidden_dim))] 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def build(self, input_shape):
		self.input_spec = [InputSpec(shape=input_shape)]
		self.input_dim = input_shape[2]

		self.W = self.init((self.output_dim, 4 * self.input_dim),
		                   name='{}_W'.format(self.name))
		self.U = self.inner_init((self.input_dim, 4 * self.input_dim),
		                         name='{}_U'.format(self.name))
		self.b = K.variable(np.hstack((np.zeros(self.input_dim),
		                               K.get_value(self.forget_bias_init((self.input_dim,))),
		                               np.zeros(self.input_dim),
		                               np.zeros(self.input_dim))),
		                    name='{}_b'.format(self.name))

		self.A = self.init((self.input_dim, self.output_dim),
		                    name='{}_A'.format(self.name))
		self.ba = K.zeros((self.output_dim,), name='{}_ba'.format(self.name))


		self.trainable_weights = [self.W, self.U, self.b, self.A, self.ba]

		if self.initial_weights is not None:
			self.set_weights(self.initial_weights)
			del self.initial_weights 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def reset_states(self):
		assert self.stateful, 'Layer must be stateful.'
		input_shape = self.input_spec[0].shape
		if not input_shape[0]:
			raise ValueError('If a RNN is stateful, it needs to know '
			                 'its batch size. Specify the batch size '
			                 'of your input tensors: \n'
			                 '- If using a Sequential model, '
			                 'specify the batch size by passing '
			                 'a `batch_input_shape` '
			                 'argument to your first layer.\n'
			                 '- If using the functional API, specify '
			                 'the time dimension by passing a '
			                 '`batch_shape` argument to your Input layer.')
		if hasattr(self, 'states'):
			K.set_value(self.states[0],
			            np.zeros((input_shape[0], self.input_dim)))
			K.set_value(self.states[1],
			            np.zeros((input_shape[0], self.output_dim)))
		else:
			self.states = [K.zeros((input_shape[0], self.input_dim)),
							K.zeros((input_shape[0], self.output_dim))] 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]

        t = K.cast(self.iterations, K.floatx()) + 1
        lr_t = self.learning_rate * (K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t)))

        ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        self.weights = [self.iterations] + ms + vs

        for p, g, m, v in zip(params, grads, ms, vs):
            m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
            p_t = lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
            self.updates.append(K.update(m, m_t))
            self.updates.append(K.update(v, v_t))
            self.updates.append(K.update_sub(p, p_t))
        return self.updates 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def build(self, input_shape):
        # Create mean and count
        # These are weights because just maintaining variables don't get saved with the model, and we'd like
        # to have these numbers saved when we save the model.
        # But we need to make sure that the weights are untrainable.
        self.mean = self.add_weight(name='mean', 
                                      shape=input_shape[1:],
                                      initializer='zeros',
                                      trainable=False)
        self.count = self.add_weight(name='count', 
                                      shape=[1],
                                      initializer='zeros',
                                      trainable=False)

        # self.mean = K.zeros(input_shape[1:], name='mean')
        # self.count = K.variable(0.0, name='count')
        super(MeanStream, self).build(input_shape)  # Be sure to call this somewhere! 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def cmc(model):
    
    def cmc_curve(model, camera1, camera2, rank_max=50):
        num = camera1.shape[0]    
        rank = []
        score = []    
        camera_batch1 = np.zeros(camera1.shape)
        for i in range(num):
            for j in range(num):
                camera_batch1[j] = camera1[i]
            similarity_batch = model.predict_on_batch([camera_batch1, camera2])
            sim_trans = similarity_batch.transpose()
            similarity_rate_sorted = np.argsort(sim_trans[0])
            for k in range(num):
                if similarity_rate_sorted[k] == i:
                    rank.append(k+1)
                    break
        rank_val = 0
        for i in range(rank_max):
            rank_val = rank_val + len([j for j in rank if i == j-1])        
            score.append(rank_val / float(num))
        return np.array(score)  
        
    a,b = get_data_for_cmc()
    return cmc_curve(model,a,b) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def call(self, x, mask=None):

            s = K.shape(x)
            b = s[0]
            r = s[1]
            c = s[2]
            ch = s[3]

            half_n = self.n // 2 # half the local region

            input_sqr = K.square(x) # square the input

            extra_channels = K.zeros((b, r, c, ch + 2 * half_n))
            input_sqr = K.concatenate([extra_channels[:, :, :, :half_n],input_sqr, extra_channels[:, :, :, half_n + ch:]], axis = 3)

            scale = self.k # offset for the scale
            norm_alpha = self.alpha / self.n # normalized alpha
            for i in range(self.n):
                scale += norm_alpha * input_sqr[:, :, :, i:i+ch]
            scale = scale ** self.beta
            x = x / scale
            
            return x 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def vectorizeData(xContext, xQuestion, xAnswerBeing, xAnswerEnd, word_index, context_maxlen, question_maxlen):
    '''Vectorize the words to their respective index and pad context to max context length and question to max question length.
       Answers vectors are padded to the max context length as well.
    '''
    X = []
    Xq = []
    YBegin = []
    YEnd = []
    for i in xrange(len(xContext)):
        x = [word_index[w] for w in xContext[i]]
        xq = [word_index[w] for w in xQuestion[i]]
        # map the first and last words of answer span to one-hot representations
        y_Begin =  np.zeros(len(xContext[i]))
        y_Begin[xAnswerBeing[i]] = 1
        y_End = np.zeros(len(xContext[i]))
        y_End[xAnswerEnd[i]] = 1
        X.append(x)
        Xq.append(xq)
        YBegin.append(y_Begin)
        YEnd.append(y_End)
    return pad_sequences(X, maxlen=context_maxlen, padding='post'), pad_sequences(Xq, maxlen=question_maxlen, padding='post'), pad_sequences(YBegin, maxlen=context_maxlen, padding='post'), pad_sequences(YEnd, maxlen=context_maxlen, padding='post')

# Note: Need to download and unzip Glove pre-train model files into same file as this script 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def build(self, input_shape):
        # input shape is (batch_size, num_words, num_senses, num_hyps)
        self.num_senses = input_shape[-2]
        self.num_hyps = input_shape[-1] - 1  # -1 because the last value is a word index
        # embedding of size 1.
        if self.set_sense_priors:
            self.sense_priors = self._get_initial_sense_priors((self.word_index_size, 1), name='{}_sense_priors'.format(self.name))
        else:
            # OntoLSTM makes sense proabilities uniform if the passed sense parameters are zero.
            self.sense_priors = K.zeros((self.word_index_size, 1))  # uniform sense probs
        # Keeping aside the initial weights to not let Embedding set them. It wouldn't know what sense priors are.
        if self.initial_weights is not None:
            self.onto_aware_embedding_weights = self.initial_weights
            self.initial_weights = None
        # The following method will set self.trainable_weights
        super(OntoAwareEmbedding, self).build(input_shape)  # input_shape will not be used by Embedding's build.
        if not self.tune_embedding:
            # Move embedding to non_trainable_weights
            self._non_trainable_weights.append(self._trainable_weights.pop())

        if self.set_sense_priors:
            self._trainable_weights.append(self.sense_priors)

        if self.onto_aware_embedding_weights is not None:
            self.set_weights(self.onto_aware_embedding_weights) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def reset_states(self):
    assert self.stateful, 'Layer must be stateful.'
    input_shape = self.input_spec[0].shape
    if not input_shape[0]:
      raise Exception('If a RNN is stateful, a complete ' +
                      'input_shape must be provided (including batch size).')
    if hasattr(self, 'states'):
      K.set_value(self.states[0],
                  np.zeros((input_shape[0], self.output_dim)))
    else:
      self.states = [K.zeros((input_shape[0], self.output_dim))] 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def build(self, input_shape):
    super(LSTM_LN, self).build(input_shape)
    self.gs, self.bs = [], []
    for i in xrange(3):
      f = 1 if i == 2 else 4
      self.gs += [ K.ones((f*self.output_dim,), name='{}_g%i'.format(self.name, i)) ]
      self.bs += [ K.zeros((f*self.output_dim,), name='{}_b%d'.format(self.name, i)) ]
    self.trainable_weights += self.gs + self.bs 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def build(self):
        stack_size = self.input_shape[2]
        dtensor5 = T.TensorType('float32', (False,)*5)
        self.input = dtensor5()
        self.W_shape = (self.nb_filter, stack_size, self.nb_row, self.nb_col)
        self.W = self.init(self.W_shape)
        self.b = shared_zeros((self.nb_filter,))

        self.params = [self.W, self.b]

        self.regularizers = []

        if self.W_regularizer:
            self.W_regularizer.set_param(self.W)
            self.regularizers.append(self.W_regularizer)

        if self.b_regularizer:
            self.b_regularizer.set_param(self.b)
            self.regularizers.append(self.b_regularizer)

        if self.activity_regularizer:
            self.activity_regularizer.set_layer(self)
            self.regularizers.append(self.activity_regularizer)

        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def reset_states(self):
        assert self.stateful, 'Layer must be stateful.'
        input_shape = self.input_spec[0].shape
        if not input_shape[0]:
            raise Exception('If a RNN is stateful, a complete ' +
                            'input_shape must be provided (including batch size).')
        if hasattr(self, 'states'):
            K.set_value(self.states[0],
                        np.zeros((input_shape[0], self.output_dim)))
            K.set_value(self.states[1],
                        np.zeros((input_shape[0], self.output_dim)))
        else:
            self.states = [K.zeros((input_shape[0], self.output_dim)),
                           K.zeros((input_shape[0], self.output_dim))] 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import zeros [as 别名]
def initial(number_of_memory_locations, memory_vector_size):
    return K.zeros((number_of_memory_locations, memory_vector_size))