Python keras.backend.int_shape() Examples
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Example #1
Source File: models.py From SeqGAN with MIT License | 6 votes |
def Highway(x, num_layers=1, activation='relu', name_prefix=''): ''' Layer wrapper function for Highway network # Arguments: x: tensor, shape = (B, input_size) # Optional Arguments: num_layers: int, dafault is 1, the number of Highway network layers activation: keras activation, default is 'relu' name_prefix: str, default is '', layer name prefix # Returns: out: tensor, shape = (B, input_size) ''' input_size = K.int_shape(x)[1] for i in range(num_layers): gate_ratio_name = '{}Highway/Gate_ratio_{}'.format(name_prefix, i) fc_name = '{}Highway/FC_{}'.format(name_prefix, i) gate_name = '{}Highway/Gate_{}'.format(name_prefix, i) gate_ratio = Dense(input_size, activation='sigmoid', name=gate_ratio_name)(x) fc = Dense(input_size, activation=activation, name=fc_name)(x) x = Lambda(lambda args: args[0] * args[2] + args[1] * (1 - args[2]), name=gate_name)([fc, x, gate_ratio]) return x
Example #2
Source File: pspnet.py From image-segmentation-keras with MIT License | 6 votes |
def pool_block(feats, pool_factor): if IMAGE_ORDERING == 'channels_first': h = K.int_shape(feats)[2] w = K.int_shape(feats)[3] elif IMAGE_ORDERING == 'channels_last': h = K.int_shape(feats)[1] w = K.int_shape(feats)[2] pool_size = strides = [ int(np.round(float(h) / pool_factor)), int(np.round(float(w) / pool_factor))] x = AveragePooling2D(pool_size, data_format=IMAGE_ORDERING, strides=strides, padding='same')(feats) x = Conv2D(512, (1, 1), data_format=IMAGE_ORDERING, padding='same', use_bias=False)(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = resize_image(x, strides, data_format=IMAGE_ORDERING) return x
Example #3
Source File: model_utils.py From image-segmentation-keras with MIT License | 6 votes |
def resize_image(inp, s, data_format): try: return Lambda(lambda x: K.resize_images(x, height_factor=s[0], width_factor=s[1], data_format=data_format, interpolation='bilinear'))(inp) except Exception as e: # if keras is old, then rely on the tf function # Sorry theano/cntk users!!! assert data_format == 'channels_last' assert IMAGE_ORDERING == 'channels_last' import tensorflow as tf return Lambda( lambda x: tf.image.resize_images( x, (K.int_shape(x)[1]*s[0], K.int_shape(x)[2]*s[1])) )(inp)
Example #4
Source File: hourglass.py From keras-centernet with MIT License | 6 votes |
def residual(_x, out_dim, name, stride=1): shortcut = _x num_channels = K.int_shape(shortcut)[-1] _x = ZeroPadding2D(padding=1, name=name + '.pad1')(_x) _x = Conv2D(out_dim, 3, strides=stride, use_bias=False, name=name + '.conv1')(_x) _x = BatchNormalization(epsilon=1e-5, name=name + '.bn1')(_x) _x = Activation('relu', name=name + '.relu1')(_x) _x = Conv2D(out_dim, 3, padding='same', use_bias=False, name=name + '.conv2')(_x) _x = BatchNormalization(epsilon=1e-5, name=name + '.bn2')(_x) if num_channels != out_dim or stride != 1: shortcut = Conv2D(out_dim, 1, strides=stride, use_bias=False, name=name + '.shortcut.0')( shortcut) shortcut = BatchNormalization(epsilon=1e-5, name=name + '.shortcut.1')(shortcut) _x = Add(name=name + '.add')([_x, shortcut]) _x = Activation('relu', name=name + '.relu')(_x) return _x
Example #5
Source File: normalizations.py From se_relativisticgan with MIT License | 6 votes |
def call(self, inputs, training=None): input_shape = K.int_shape(inputs) reduction_axes = list(range(0, len(input_shape))) if (self.axis is not None): del reduction_axes[self.axis] del reduction_axes[0] mean = K.mean(inputs, reduction_axes, keepdims=True) stddev = K.std(inputs, reduction_axes, keepdims=True) + self.epsilon normed = (inputs - mean) / stddev broadcast_shape = [1] * len(input_shape) if self.axis is not None: broadcast_shape[self.axis] = input_shape[self.axis] if self.scale: broadcast_gamma = K.reshape(self.gamma, broadcast_shape) normed = normed * broadcast_gamma if self.center: broadcast_beta = K.reshape(self.beta, broadcast_shape) normed = normed + broadcast_beta return normed
Example #6
Source File: test_blocks.py From keras-fcn with MIT License | 6 votes |
def test_vgg_conv(): if K.image_data_format() == 'channels_first': x = Input(shape=(3, 224, 224)) y1_shape = (None, 64, 112, 112) y2_shape = (None, 128, 56, 56) else: x = Input(shape=(224, 224, 3)) y1_shape = (None, 112, 112, 64) y2_shape = (None, 56, 56, 128) block1 = vgg_conv(filters=64, convs=2, block_name='block1') y = block1(x) assert K.int_shape(y) == y1_shape block2 = vgg_conv(filters=128, convs=2, block_name='block2') y = block2(y) assert K.int_shape(y) == y2_shape
Example #7
Source File: timeception.py From deep-smoke-machine with BSD 3-Clause "New" or "Revised" License | 6 votes |
def timeception_layers(tensor, n_layers=4, n_groups=8, is_dilated=True): input_shape = K.int_shape(tensor) assert len(input_shape) == 5 expansion_factor = 1.25 _, n_timesteps, side_dim, side_dim, n_channels_in = input_shape # how many layers of timeception for i in range(n_layers): layer_num = i + 1 # get details about grouping n_channels_per_branch, n_channels_out = __get_n_channels_per_branch(n_groups, expansion_factor, n_channels_in) # temporal conv per group tensor = __grouped_convolutions(tensor, n_groups, n_channels_per_branch, is_dilated, layer_num) # downsample over time tensor = MaxPooling3D(pool_size=(2, 1, 1), name='maxpool_tc%d' % (layer_num))(tensor) n_channels_in = n_channels_out return tensor
Example #8
Source File: timeception.py From deep-smoke-machine with BSD 3-Clause "New" or "Revised" License | 6 votes |
def __call_timeception_layers(self, tensor, n_layers, n_groups, expansion_factor): input_shape = K.int_shape(tensor) assert len(input_shape) == 5 _, n_timesteps, side_dim, side_dim, n_channels_in = input_shape # how many layers of timeception for i in range(n_layers): layer_num = i + 1 # get details about grouping n_channels_per_branch, n_channels_out = self.__get_n_channels_per_branch(n_groups, expansion_factor, n_channels_in) # temporal conv per group tensor = self.__call_grouped_convolutions(tensor, n_groups, n_channels_per_branch, layer_num) # downsample over time tensor = getattr(self, 'maxpool_tc%d' % (layer_num))(tensor) n_channels_in = n_channels_out return tensor
Example #9
Source File: chapter_06_001.py From Python-Deep-Learning-SE with MIT License | 6 votes |
def sampling(args: tuple): """ Reparameterization trick by sampling z from unit Gaussian :param args: (tensor, tensor) mean and log of variance of q(z|x) :returns tensor: sampled latent vector z """ # unpack the input tuple z_mean, z_log_var = args # mini-batch size mb_size = K.shape(z_mean)[0] # latent space size dim = K.int_shape(z_mean)[1] # random normal vector with mean=0 and std=1.0 epsilon = K.random_normal(shape=(mb_size, dim)) return z_mean + K.exp(0.5 * z_log_var) * epsilon
Example #10
Source File: test_decoders.py From keras-fcn with MIT License | 6 votes |
def test_vgg_decoder(): if K.image_data_format() == 'channels_last': inputs = Input(shape=(500, 500, 3)) pool3 = Input(shape=(88, 88, 256)) pool4 = Input(shape=(44, 44, 512)) drop7 = Input(shape=(16, 16, 4096)) score_shape = (None, 500, 500, 21) else: inputs = Input(shape=(3, 500, 500)) pool3 = Input(shape=(256, 88, 88)) pool4 = Input(shape=(512, 44, 44)) drop7 = Input(shape=(4096, 16, 16)) score_shape = (None, 21, 500, 500) pyramid = [drop7, pool4, pool3, inputs] scales = [1., 1e-2, 1e-4] score = VGGDecoder(pyramid, scales, classes=21) assert K.int_shape(score) == score_shape
Example #11
Source File: decoders.py From keras-fcn with MIT License | 6 votes |
def VGGUpsampler(pyramid, scales, classes, weight_decay=0.): """A Functional upsampler for the VGG Nets. :param: pyramid: A list of features in pyramid, scaling from large receptive field to small receptive field. The bottom of the pyramid is the input image. :param: scales: A list of weights for each of the feature map in the pyramid, sorted in the same order as the pyramid. :param: classes: Integer, number of classes. """ if len(scales) != len(pyramid) - 1: raise ValueError('`scales` needs to match the length of' '`pyramid` - 1.') blocks = [] for i in range(len(pyramid) - 1): block_name = 'feat{}'.format(i + 1) block = vgg_upsampling(classes=classes, target_shape=K.int_shape(pyramid[i + 1]), scale=scales[i], weight_decay=weight_decay, block_name=block_name) blocks.append(block) return Decoder(pyramid=pyramid[:-1], blocks=blocks)
Example #12
Source File: timeception.py From timeception with GNU General Public License v3.0 | 6 votes |
def timeception_layers(tensor, n_layers=4, n_groups=8, is_dilated=True): input_shape = K.int_shape(tensor) assert len(input_shape) == 5 expansion_factor = 1.25 _, n_timesteps, side_dim, side_dim, n_channels_in = input_shape # how many layers of timeception for i in range(n_layers): layer_num = i + 1 # get details about grouping n_channels_per_branch, n_channels_out = __get_n_channels_per_branch(n_groups, expansion_factor, n_channels_in) # temporal conv per group tensor = __grouped_convolutions(tensor, n_groups, n_channels_per_branch, is_dilated, layer_num) # downsample over time tensor = MaxPooling3D(pool_size=(2, 1, 1), name='maxpool_tc%d' % (layer_num))(tensor) n_channels_in = n_channels_out return tensor
Example #13
Source File: instance_normalization.py From Coloring-greyscale-images with MIT License | 6 votes |
def call(self, inputs, training=None): input_shape = K.int_shape(inputs) reduction_axes = list(range(0, len(input_shape))) if (self.axis is not None): del reduction_axes[self.axis] del reduction_axes[0] mean = K.mean(inputs, reduction_axes, keepdims=True) stddev = K.std(inputs, reduction_axes, keepdims=True) + self.epsilon normed = (inputs - mean) / stddev broadcast_shape = [1] * len(input_shape) if self.axis is not None: broadcast_shape[self.axis] = input_shape[self.axis] if self.scale: broadcast_gamma = K.reshape(self.gamma, broadcast_shape) normed = normed * broadcast_gamma if self.center: broadcast_beta = K.reshape(self.beta, broadcast_shape) normed = normed + broadcast_beta return normed
Example #14
Source File: vae.py From pyod with BSD 2-Clause "Simplified" License | 6 votes |
def sampling(self, args): """Reparametrisation by sampling from Gaussian, N(0,I) To sample from epsilon = Norm(0,I) instead of from likelihood Q(z|X) with latent variables z: z = z_mean + sqrt(var) * epsilon Parameters ---------- args : tensor Mean and log of variance of Q(z|X). Returns ------- z : tensor Sampled latent variable. """ z_mean, z_log = args batch = K.shape(z_mean)[0] # batch size dim = K.int_shape(z_mean)[1] # latent dimension epsilon = K.random_normal(shape=(batch, dim)) # mean=0, std=1.0 return z_mean + K.exp(0.5 * z_log) * epsilon
Example #15
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 #16
Source File: timeception.py From timeception with GNU General Public License v3.0 | 6 votes |
def __call_timeception_layers(self, tensor, n_layers, n_groups, expansion_factor): input_shape = K.int_shape(tensor) assert len(input_shape) == 5 _, n_timesteps, side_dim, side_dim, n_channels_in = input_shape # how many layers of timeception for i in range(n_layers): layer_num = i + 1 # get details about grouping n_channels_per_branch, n_channels_out = self.__get_n_channels_per_branch(n_groups, expansion_factor, n_channels_in) # temporal conv per group tensor = self.__call_grouped_convolutions(tensor, n_groups, n_channels_per_branch, layer_num) # downsample over time tensor = getattr(self, 'maxpool_tc%d' % (layer_num))(tensor) n_channels_in = n_channels_out return tensor
Example #17
Source File: models.py From vaegan-celebs-keras with MIT License | 5 votes |
def _sampling(args): """Reparameterization trick by sampling fr an isotropic unit Gaussian. Instead of sampling from Q(z|X), sample eps = N(0,I) # Arguments: args (tensor): mean and log of variance of Q(z|X) # Returns: z (tensor): sampled latent vector """ z_mean, z_log_var = args batch = K.shape(z_mean)[0] dim = K.int_shape(z_mean)[1] # by default, random_normal has mean=0 and std=1.0 epsilon = K.random_normal(shape=(batch, dim)) return z_mean + K.exp(0.5 * z_log_var) * epsilon
Example #18
Source File: mixnets.py From keras_mixnets with MIT License | 5 votes |
def __call__(self, inputs): filters = K.int_shape(inputs)[self._channel_axis] grouped_op = GroupConvolution(filters, self.kernels, groups=len(self.kernels), type='depthwise_conv', conv_kwargs=self._conv_kwargs) x = grouped_op(inputs) return x # Obtained from https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/mixnet_model.py
Example #19
Source File: losses.py From vaegan-celebs-keras with MIT License | 5 votes |
def mean_gaussian_negative_log_likelihood(y_true, y_pred): nll = 0.5 * np.log(2 * np.pi) + 0.5 * K.square(y_pred - y_true) axis = tuple(range(1, len(K.int_shape(y_true)))) return K.mean(K.sum(nll, axis=axis), axis=-1)
Example #20
Source File: model_store.py From imgclsmob with MIT License | 5 votes |
def _preprocess_weights_for_loading(layer, weights): """ Converts layers weights. Parameters ---------- layer : Layer Layer instance. weights : list of np.array List of weights values. Returns ------- list of np.array A list of weights values. """ is_channels_first = (K.image_data_format() == "channels_first") if ((K.backend() == "mxnet") and (not is_channels_first)) or (K.backend() == "tensorflow"): if layer.__class__.__name__ == "Conv2D": weights[0] = np.transpose(weights[0], (2, 3, 1, 0)) elif layer.__class__.__name__ == "DepthwiseConv2D": weights[0] = np.transpose(weights[0], (2, 3, 0, 1)) for i in range(len(weights)): assert (K.int_shape(layer.weights[i]) == weights[i].shape) return weights
Example #21
Source File: model.py From Mask-RCNN-Pedestrian-Detection with MIT License | 5 votes |
def mrcnn_bbox_loss_graph(target_bbox, target_class_ids, pred_bbox): """Loss for Mask R-CNN bounding box refinement. target_bbox: [batch, num_rois, (dy, dx, log(dh), log(dw))] target_class_ids: [batch, num_rois]. Integer class IDs. pred_bbox: [batch, num_rois, num_classes, (dy, dx, log(dh), log(dw))] """ # Reshape to merge batch and roi dimensions for simplicity. target_class_ids = K.reshape(target_class_ids, (-1,)) target_bbox = K.reshape(target_bbox, (-1, 4)) pred_bbox = K.reshape(pred_bbox, (-1, K.int_shape(pred_bbox)[2], 4)) # Only positive ROIs contribute to the loss. And only # the right class_id of each ROI. Get their indicies. positive_roi_ix = tf.where(target_class_ids > 0)[:, 0] positive_roi_class_ids = tf.cast( tf.gather(target_class_ids, positive_roi_ix), tf.int64) indices = tf.stack([positive_roi_ix, positive_roi_class_ids], axis=1) # Gather the deltas (predicted and true) that contribute to loss target_bbox = tf.gather(target_bbox, positive_roi_ix) pred_bbox = tf.gather_nd(pred_bbox, indices) # Smooth-L1 Loss loss = K.switch(tf.size(target_bbox) > 0, smooth_l1_loss(y_true=target_bbox, y_pred=pred_bbox), tf.constant(0.0)) loss = K.mean(loss) return loss
Example #22
Source File: FixedBatchNormalization.py From keras-frcnn with Apache License 2.0 | 5 votes |
def call(self, x, mask=None): assert self.built, 'Layer must be built before being called' input_shape = K.int_shape(x) reduction_axes = list(range(len(input_shape))) del reduction_axes[self.axis] broadcast_shape = [1] * len(input_shape) broadcast_shape[self.axis] = input_shape[self.axis] if sorted(reduction_axes) == range(K.ndim(x))[:-1]: x_normed = K.batch_normalization( x, self.running_mean, self.running_std, self.beta, self.gamma, epsilon=self.epsilon) else: # need broadcasting broadcast_running_mean = K.reshape(self.running_mean, broadcast_shape) broadcast_running_std = K.reshape(self.running_std, broadcast_shape) broadcast_beta = K.reshape(self.beta, broadcast_shape) broadcast_gamma = K.reshape(self.gamma, broadcast_shape) x_normed = K.batch_normalization( x, broadcast_running_mean, broadcast_running_std, broadcast_beta, broadcast_gamma, epsilon=self.epsilon) return x_normed
Example #23
Source File: model.py From EasyPR-python with Apache License 2.0 | 5 votes |
def mrcnn_bbox_loss_graph(target_bbox, target_class_ids, pred_bbox): """Loss for Mask R-CNN bounding box refinement. target_bbox: [batch, num_rois, (dy, dx, log(dh), log(dw))] target_class_ids: [batch, num_rois]. Integer class IDs. pred_bbox: [batch, num_rois, num_classes, (dy, dx, log(dh), log(dw))] """ # Reshape to merge batch and roi dimensions for simplicity. target_class_ids = K.reshape(target_class_ids, (-1,)) target_bbox = K.reshape(target_bbox, (-1, 4)) pred_bbox = K.reshape(pred_bbox, (-1, K.int_shape(pred_bbox)[2], 4)) # Only positive ROIs contribute to the loss. And only # the right class_id of each ROI. Get their indicies. positive_roi_ix = tf.where(target_class_ids > 0)[:, 0] positive_roi_class_ids = tf.cast(tf.gather(target_class_ids, positive_roi_ix), tf.int64) indices = tf.stack([positive_roi_ix, positive_roi_class_ids], axis=1) # Gather the deltas (predicted and true) that contribute to loss target_bbox = tf.gather(target_bbox, positive_roi_ix) pred_bbox = tf.gather_nd(pred_bbox, indices) # Smooth-L1 Loss loss = K.switch(tf.size(target_bbox) > 0, smooth_l1_loss(y_true=target_bbox, y_pred=pred_bbox), tf.constant(0.0)) loss = K.mean(loss) loss = K.reshape(loss, [1, 1]) return loss
Example #24
Source File: wtte.py From wtte-rnn with MIT License | 5 votes |
def _keras_unstack_hack(ab): """Implements tf.unstack(y_true_keras, num=2, axis=-1). Keras-hack adopted to be compatible with Theano backend. :param ab: stacked variables :return a, b: unstacked variables """ ndim = len(K.int_shape(ab)) if ndim == 0: print('can not unstack with ndim=0') else: a = ab[..., 0] b = ab[..., 1] return a, b
Example #25
Source File: FixedBatchNormalization.py From Keras-FasterRCNN with MIT License | 5 votes |
def call(self, x, mask=None): assert self.built, 'Layer must be built before being called' input_shape = K.int_shape(x) reduction_axes = list(range(len(input_shape))) del reduction_axes[self.axis] broadcast_shape = [1] * len(input_shape) broadcast_shape[self.axis] = input_shape[self.axis] if sorted(reduction_axes) == range(K.ndim(x))[:-1]: x_normed = K.batch_normalization( x, self.running_mean, self.running_std, self.beta, self.gamma, epsilon=self.epsilon) else: # need broadcasting broadcast_running_mean = K.reshape(self.running_mean, broadcast_shape) broadcast_running_std = K.reshape(self.running_std, broadcast_shape) broadcast_beta = K.reshape(self.beta, broadcast_shape) broadcast_gamma = K.reshape(self.gamma, broadcast_shape) x_normed = K.batch_normalization( x, broadcast_running_mean, broadcast_running_std, broadcast_beta, broadcast_gamma, epsilon=self.epsilon) return x_normed
Example #26
Source File: rnnrbm.py From keras_bn_library with MIT License | 5 votes |
def preprocess_input(self, x): if self.consume_less == 'cpu': input_shape = K.int_shape(x) input_dim = input_shape[2] timesteps = input_shape[1] return time_distributed_dense(x, self.W, self.b, self.dropout_W, input_dim, self.hidden_recurrent_dim, timesteps) else: return x
Example #27
Source File: gc.py From keras-global-context-networks with MIT License | 5 votes |
def _spatial_flattenND(ip, rank): assert rank in [3, 4, 5], "Rank of input must be 3, 4 or 5" ip_shape = K.int_shape(ip) channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 if rank == 3: x = ip # identity op for rank 3 elif rank == 4: if channel_dim == 1: # [C, D1, D2] -> [C, D1 * D2] shape = [ip_shape[1], ip_shape[2] * ip_shape[3]] else: # [D1, D2, C] -> [D1 * D2, C] shape = [ip_shape[1] * ip_shape[2], ip_shape[3]] x = Reshape(shape)(ip) else: if channel_dim == 1: # [C, D1, D2, D3] -> [C, D1 * D2 * D3] shape = [ip_shape[1], ip_shape[2] * ip_shape[3] * ip_shape[4]] else: # [D1, D2, D3, C] -> [D1 * D2 * D3, C] shape = [ip_shape[1] * ip_shape[2] * ip_shape[3], ip_shape[4]] x = Reshape(shape)(ip) return x
Example #28
Source File: inception.py From keras-face-recognition with MIT License | 5 votes |
def _inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): channel_axis = 3 if block_idx is None: prefix = None else: prefix = '_'.join((block_type, str(block_idx))) name_fmt = partial(_generate_layer_name, prefix=prefix) if block_type == 'Block35': branch_0 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_1x1', 0)) branch_1 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 1)) branch_1 = conv2d_bn(branch_1, 32, 3, name=name_fmt('Conv2d_0b_3x3', 1)) branch_2 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 2)) branch_2 = conv2d_bn(branch_2, 32, 3, name=name_fmt('Conv2d_0b_3x3', 2)) branch_2 = conv2d_bn(branch_2, 32, 3, name=name_fmt('Conv2d_0c_3x3', 2)) branches = [branch_0, branch_1, branch_2] elif block_type == 'Block17': branch_0 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_1x1', 0)) branch_1 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_0a_1x1', 1)) branch_1 = conv2d_bn(branch_1, 128, [1, 7], name=name_fmt('Conv2d_0b_1x7', 1)) branch_1 = conv2d_bn(branch_1, 128, [7, 1], name=name_fmt('Conv2d_0c_7x1', 1)) branches = [branch_0, branch_1] elif block_type == 'Block8': branch_0 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_1x1', 0)) branch_1 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_0a_1x1', 1)) branch_1 = conv2d_bn(branch_1, 192, [1, 3], name=name_fmt('Conv2d_0b_1x3', 1)) branch_1 = conv2d_bn(branch_1, 192, [3, 1], name=name_fmt('Conv2d_0c_3x1', 1)) branches = [branch_0, branch_1] mixed = Concatenate(axis=channel_axis, name=name_fmt('Concatenate'))(branches) up = conv2d_bn(mixed,K.int_shape(x)[channel_axis],1,activation=None,use_bias=True, name=name_fmt('Conv2d_1x1')) up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': scale})(up) x = add([x, up]) if activation is not None: x = Activation(activation, name=name_fmt('Activation'))(x) return x
Example #29
Source File: funcs.py From BERT-keras with GNU General Public License v3.0 | 5 votes |
def shape_list(x): if K.backend() != 'theano': tmp = K.int_shape(x) else: tmp = x.shape tmp = list(tmp) tmp[0] = -1 return tmp
Example #30
Source File: bert.py From keras-bert-ner with MIT License | 5 votes |
def call(self, inputs): if not hasattr(self, "kernel"): embedding_layer = inputs._keras_history[0] if embedding_layer.name != self.embedding_name: def recursive_search(layer): """递归向上搜索,根据名字找Embedding层 """ last_layer = layer._inbound_nodes[0].inbound_layers if isinstance(last_layer, list): if len(last_layer) == 0: return None else: last_layer = last_layer[0] if last_layer.name == self.embedding_name: return last_layer else: return recursive_search(last_layer) embedding_layer = recursive_search(embedding_layer) if embedding_layer is None: raise Exception("Embedding layer not found") self.kernel = K.transpose(embedding_layer.embeddings) self.units = K.int_shape(self.kernel)[1] self.bias = self.add_weight(name="bias", shape=(self.units, ), initializer="zeros") outputs = K.dot(inputs, self.kernel) outputs = K.bias_add(outputs, self.bias) outputs = self.activation(outputs) return outputs