Python backend.repeat_elements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 repeat_elements [as 别名]
def call(self, x):
        mean = K.mean(x, axis=-1)
        std = K.std(x, axis=-1)

        if len(x.shape) == 3:
            mean = K.permute_dimensions(
                K.repeat(mean, x.shape.as_list()[-1]),
                [0,2,1]
            )
            std = K.permute_dimensions(
                K.repeat(std, x.shape.as_list()[-1]),
                [0,2,1] 
            )
            
        elif len(x.shape) == 2:
            mean = K.reshape(
                K.repeat_elements(mean, x.shape.as_list()[-1], 0),
                (-1, x.shape.as_list()[-1])
            )
            std = K.reshape(
                K.repeat_elements(mean, x.shape.as_list()[-1], 0),
                (-1, x.shape.as_list()[-1])
            )
        
        return self._g * (x - mean) / (std + self._epsilon) + self._b 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def call(self, x, mask=None):
        if K.image_dim_ordering == "th":
            _, f, r, c = self.shape
        else:
            _, r, c, f = self.shape
        squared = K.square(x)
        pooled = K.pool2d(squared, (self.n, self.n), strides=(1, 1),
            padding="same", pool_mode="avg")
        if K.image_dim_ordering == "th":
            summed = K.sum(pooled, axis=1, keepdims=True)
            averaged = self.alpha * K.repeat_elements(summed, f, axis=1)
        else:
            summed = K.sum(pooled, axis=3, keepdims=True)
            averaged = self.alpha * K.repeat_elements(summed, f, axis=3)
        denom = K.pow(self.k + averaged, self.beta)
        return x / denom 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def call(self, inputs, training=None):
        inputs_expand = K.expand_dims(inputs, 1)
        
        inputs_tiled = K.tile(inputs_expand, [1, self.num_capsule, 1, 1])
        
        if(self.channels!=0):
            W2 = K.repeat_elements(self.W,int(self.input_num_capsule/self.channels),1)
        else:
            W2 = self.W
            
        inputs_hat = K.map_fn(lambda x: K.batch_dot(x, W2, [2, 3]) , elems=inputs_tiled)

        b = tf.zeros(shape=[K.shape(inputs_hat)[0], self.num_capsule, self.input_num_capsule])

        assert self.routings > 0, 'The routings should be > 0.'
        for i in range(self.routings):

            c = tf.nn.softmax(b, dim=1)
            outputs = squash(K.batch_dot(c, inputs_hat, [2, 2])+ self.B)

            if i < self.routings - 1:
                b += K.batch_dot(outputs, inputs_hat, [2, 3])

        return outputs 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def make_warped_stack(args):
    mask = args[0]
    src_in = args[1]
    trans_in = args[2]

    for i in range(11):
        mask_i = K.repeat_elements(tf.expand_dims(mask[:, :, :, i], 3), 3, 3)
        src_masked = tf.multiply(mask_i, src_in)

        if i == 0:
            warps = src_masked
        else:
            warp_i = affine_warp(src_masked, trans_in[:, :, :, i])
            warps = tf.concat([warps, warp_i], 3)

    return warps 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def call(self, input_tensor, mask=None):
        x = input_tensor[0]
        y = input_tensor[1]
        mask = mask[0]

        y = K.transpose(K.dot(self.W, K.transpose(y)))
        y = K.expand_dims(y, axis=-2)
        y = K.repeat_elements(y, self.steps, axis=1)
        eij = K.sum(x * y, axis=-1)

        if self.bias:
            b = K.repeat_elements(self.b, self.steps, axis=0)
            eij += b

        eij = K.tanh(eij)
        a = K.exp(eij)

        if mask is not None:
            a *= K.cast(mask, K.floatx())

        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
        return a 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def QRNcell():
    xq = Input(batch_shape=(batch_size, embedding_dim * 2))
    # Split into context and query
    xt = Lambda(lambda x, dim: x[:, :dim], arguments={'dim': embedding_dim},
                output_shape=lambda s: (s[0], s[1] / 2))(xq)
    qt = Lambda(lambda x, dim: x[:, dim:], arguments={'dim': embedding_dim},
                output_shape=lambda s: (s[0], s[1] / 2))(xq)

    h_tm1 = Input(batch_shape=(batch_size, embedding_dim))

    zt = Dense(1, activation='sigmoid', bias_initializer=Constant(2.5))(multiply([xt, qt]))
    zt = Lambda(lambda x, dim: K.repeat_elements(x, dim, axis=1), arguments={'dim': embedding_dim})(zt)
    ch = Dense(embedding_dim, activation='tanh')(concatenate([xt, qt], axis=-1))
    rt = Dense(1, activation='sigmoid')(multiply([xt, qt]))
    rt = Lambda(lambda x, dim: K.repeat_elements(x, dim, axis=1), arguments={'dim': embedding_dim})(rt)
    ht = add([multiply([zt, ch, rt]), multiply([Lambda(lambda x: 1 - x, output_shape=lambda s: s)(zt), h_tm1])])
    return RecurrentModel(input=xq, output=ht, initial_states=[h_tm1], final_states=[ht], return_sequences=True)


#
# Load data
# 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def kl_divergence(y_true, y_pred):
    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=2), axis=2)), 
                                                                   shape_r_out, axis=-1)), shape_c_out, axis=-1)
    y_pred /= max_y_pred

    sum_y_true = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_true, axis=2), axis=2)), 
                                                                   shape_r_out, axis=-1)), shape_c_out, axis=-1)
    sum_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_pred, axis=2), axis=2)), 
                                                                   shape_r_out, axis=-1)), shape_c_out, axis=-1)
    y_true /= (sum_y_true + K.epsilon())
    y_pred /= (sum_y_pred + K.epsilon())

    return 10 * K.sum(K.sum(y_true * K.log((y_true / (y_pred + K.epsilon())) + K.epsilon()), axis=-1), axis=-1)


# Correlation Coefficient Loss 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def nss(y_true, y_pred):
    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=2), axis=2)), 
                                                                   shape_r_out, axis=-1)), shape_c_out, axis=-1)
    y_pred /= max_y_pred
    y_pred_flatten = K.batch_flatten(y_pred)

    y_mean = K.mean(y_pred_flatten, axis=-1)
    y_mean = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.expand_dims(y_mean)), 
                                                               shape_r_out, axis=-1)), shape_c_out, axis=-1)

    y_std = K.std(y_pred_flatten, axis=-1)
    y_std = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.expand_dims(y_std)), 
                                                              shape_r_out, axis=-1)), shape_c_out, axis=-1)

    y_pred = (y_pred - y_mean) / (y_std + K.epsilon())

    return -(K.sum(K.sum(y_true * y_pred, axis=2), axis=2) / K.sum(K.sum(y_true, axis=2), axis=2))


# Gaussian priors initialization 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def step(self, x, states):
        x_shape = K.shape(x)
        h_tm1 = states[0]
        c_tm1 = states[1]

        e = self.V_a(K.tanh(self.W_a(h_tm1) + self.U_a(x)))
        a = K.reshape(K.softmax(K.batch_flatten(e)), (x_shape[0], 1, x_shape[2], x_shape[3]))
        x_tilde = x * K.repeat_elements(a, x_shape[1], 1)

        x_i = self.W_i(x_tilde)
        x_f = self.W_f(x_tilde)
        x_c = self.W_c(x_tilde)
        x_o = self.W_o(x_tilde)

        i = self.inner_activation(x_i + self.U_i(h_tm1))
        f = self.inner_activation(x_f + self.U_f(h_tm1))
        c = f * c_tm1 + i * self.activation(x_c + self.U_c(h_tm1))
        o = self.inner_activation(x_o + self.U_o(h_tm1))

        h = o * self.activation(c)
        return h, [h, c] 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def test_repeat_elements(self):
        reps = 3
        for ndims in [1, 2, 3]:
            shape = np.arange(2, 2 + ndims)
            arr = np.arange(np.prod(shape)).reshape(shape)

            for rep_axis in range(ndims):
                np_rep = np.repeat(arr, reps, axis=rep_axis)
                check_single_tensor_operation('repeat_elements', arr, BACKENDS,
                                              rep=reps, axis=rep_axis,
                                              assert_value_with_ref=np_rep)

                if K.backend() != 'cntk':
                    shape = list(shape)
                    shape[rep_axis] = None
                    x = K.placeholder(shape=shape)
                    y = K.repeat_elements(x, reps, axis=rep_axis)
                    assert y._keras_shape == tuple(shape)
                    assert y._keras_shape == K.int_shape(y) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def call(self, input_tensor, mask=None):
        x = input_tensor[0]
        aspect = input_tensor[1]
        mask = mask[0]

        aspect = K.transpose(K.dot(self.W, K.transpose(aspect)))
        aspect = K.expand_dims(aspect, axis=-2)
        aspect = K.repeat_elements(aspect, self.steps, axis=1)
        eij = K.sum(x*aspect, axis=-1)

        if self.bias:
            b = K.repeat_elements(self.b, self.steps, axis=0)
            eij += b

        eij = K.tanh(eij)

        a = K.exp(eij)

        if mask is not None:
            a *= K.cast(mask, K.floatx())

        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())

        return a 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def step(self, inputs, states):
        h_tm1 = states[0]  # previous memory
        #B_U = states[1]  # dropout matrices for recurrent units
        #B_W = states[2]
        h_tm1a = K.dot(h_tm1, self.Wa)
        eij = K.dot(K.tanh(h_tm1a + K.dot(inputs[:, :self.h_dim], self.Ua)), self.Va)
        eijs = K.repeat_elements(eij, self.h_dim, axis=1)

        #alphaij = K.softmax(eijs) # batchsize * lenh       h batchsize * lenh * ndim
        #ci = K.permute_dimensions(K.permute_dimensions(self.h, [2,0,1]) * alphaij, [1,2,0])
        #cisum = K.sum(ci, axis=1)
        cisum = eijs*inputs[:, :self.h_dim]
        #print(K.shape(cisum), cisum.shape, ci.shape, self.h.shape, alphaij.shape, x.shape)

        zr = K.sigmoid(K.dot(inputs[:, self.h_dim:], self.Wzr) + K.dot(h_tm1, self.Uzr) + K.dot(cisum, self.Czr))
        zi = zr[:, :self.units]
        ri = zr[:, self.units: 2 * self.units]
        si_ = K.tanh(K.dot(inputs[:, self.h_dim:], self.W) + K.dot(ri*h_tm1, self.U) + K.dot(cisum, self.C))
        si = (1-zi) * h_tm1 + zi * si_
        return si, [si] #h_tm1, [h_tm1] 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def call(self, inputs, mask=None):
        """
        Extract the GRU output for the target document index for the forward
        and backwards GRU outputs, and then concatenate them. If the target word index
        is at index l, and there are T total document words, the desired output
        in the forward pass is at GRU_f[l] (ignoring the batched case) and the
        desired output of the backwards pass is at GRU_b[T-l].

        We need to get these two vectors and concatenate them. To do so, we'll
        reverse the backwards GRU, which allows us to use the same index/mask for both.
        """
        # TODO(nelson): deal with case where cloze token appears multiple times
        # in a question.
        word_indices, gru_f, gru_b = inputs
        index_mask = K.cast(K.equal((K.ones_like(word_indices) * self.target_index),
                                    word_indices), "float32")
        gru_mask = K.repeat_elements(K.expand_dims(index_mask, -1), K.int_shape(gru_f)[-1], K.ndim(gru_f) - 1)
        masked_gru_f = switch(gru_mask, gru_f, K.zeros_like(gru_f))
        selected_gru_f = K.sum(masked_gru_f, axis=1)
        masked_gru_b = switch(gru_mask, gru_b, K.zeros_like(gru_b))
        selected_gru_b = K.sum(masked_gru_b, axis=1)
        selected_bigru = K.concatenate([selected_gru_f, selected_gru_b], axis=-1)
        return selected_bigru 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def call(self, v, **kwargs):
        assert (len(self.input_dims) == len(self.output_dims) and
                self.input_dims[0] == self.output_dims[0])

        # possibly shrink spatial axis by pooling elements
        if len(self.input_dims) == 4 and (self.input_dims[1] > self.output_dims[1] or self.input_dims[2] > self.output_dims[2]):
            assert (self.input_dims[1] % self.output_dims[1] == 0 and
                    self.input_dims[2] % self.output_dims[2] == 0)

            pool_sizes = (self.input_dims[1] / self.output_dims[1],
                          self.input_dims[2] / self.output_dims[2])
            strides = pool_sizes
            v = K.pool2d(
                v, pool_size=pool_sizes, strides=strides,
                padding='same', data_format='channels_last', pool_mode='avg')

        # possibly extend spatial axis by repeating elements
        for i in range(1, len(self.input_dims) - 1):
            if self.input_dims[i] < self.output_dims[i]:
                assert self.output_dims[i] % self.input_dims[i] == 0
                v = K.repeat_elements(
                    v, rep=int(self.output_dims[i] / self.input_dims[i]),
                    axis=i)

        return v 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import repeat_elements [as 别名]
def call(self, x, mask=None):
        a = x[0]
        b = x[1]

        a = K.mean(a, axis=0, keepdims=True)
        b = K.mean(b, axis=0, keepdims=True)
        a /= K.sum(a, keepdims=True)
        b /= K.sum(b, keepdims=True)

        a = K.clip(a, K.epsilon(), 1)
        b = K.clip(b, K.epsilon(), 1)

        loss = K.sum(a*K.log(a/b), axis=-1, keepdims=True) \
            + K.sum(b*K.log(b/a), axis=-1, keepdims=True)

        loss = K.repeat_elements(loss, self.batch_size, axis=0)
        
        return loss