def build_model(self, filters, filter_length, optimizer='rmsprop', loss='mse'): input_ = Input(shape=(self.time_step, self.input_size)) conv_h1 = Conv1D(filters, filter_length, padding='causal', activation='relu')(input_) conv_h2 = Conv1D(filters, filter_length, padding='causal', activation='relu')(conv_h1) conv_h3 = Conv1D(filters, filter_length, padding='causal', activation='relu')(conv_h2) conv_g3 = Conv1D(filters, filter_length, padding='causal', activation='relu')(conv_h3) merge_h2_g3 = add([conv_h2, conv_g3]) conv_g2 = Conv1D(filters, filter_length, padding='causal', activation='relu')(merge_h2_g3) merge_h1_g2 = add([conv_h1, conv_g2]) conv_g1 = Conv1D(filters, filter_length, padding='causal', activation='relu')(merge_h1_g2) conv_g0 = Conv1D(self.output_size, filter_length, padding='causal', activation='relu')(conv_g1) model = Model(input_, conv_g0) model.compile(optimizer, loss) model.summary() return model
def atari_qnet(input_shape, num_actions, net_name, net_size): net_name = net_name.lower() # input state state = Input(shape=input_shape) # convolutional layers conv1_32 = Conv2D(32, (8, 8), strides=(4, 4), activation='relu') conv2_64 = Conv2D(64, (4, 4), strides=(2, 2), activation='relu') conv3_64 = Conv2D(64, (3, 3), strides=(1, 1), activation='relu') # if recurrent net then change input shape if 'drqn' in net_name: # recurrent net (drqn) lambda_perm_state = lambda x: K.permute_dimensions(x, [0, 3, 1, 2]) perm_state = Lambda(lambda_perm_state)(state) dist_state = Lambda(lambda x: K.stack([x], axis=4))(perm_state) # extract features with `TimeDistributed` wrapped convolutional layers dist_conv1 = TimeDistributed(conv1_32)(dist_state) dist_conv2 = TimeDistributed(conv2_64)(dist_conv1) dist_convf = TimeDistributed(conv3_64)(dist_conv2) feature = TimeDistributed(Flatten())(dist_convf) elif 'dqn' in net_name: # fully connected net (dqn) # extract features with convolutional layers conv1 = conv1_32(state) conv2 = conv2_64(conv1) convf = conv3_64(conv2) feature = Flatten()(convf) # network type. Dense for dqn; LSTM or GRU for drqn if 'lstm' in net_name: net_type = LSTM elif 'gru' in net_name: net_type = GRU else: net_type = Dense # dueling or regular dqn/drqn if 'dueling' in net_name: value1 = net_type(net_size, activation='relu')(feature) adv1 = net_type(net_size, activation='relu')(feature) value2 = Dense(1)(value1) adv2 = Dense(num_actions)(adv1) mean_adv2 = Lambda(lambda x: K.mean(x, axis=1))(adv2) ones = K.ones([1, num_actions]) lambda_exp = lambda x: K.dot(K.expand_dims(x, axis=1), -ones) exp_mean_adv2 = Lambda(lambda_exp)(mean_adv2) sum_adv = add([exp_mean_adv2, adv2]) exp_value2 = Lambda(lambda x: K.dot(x, ones))(value2) q_value = add([exp_value2, sum_adv]) else: hid = net_type(net_size, activation='relu')(feature) q_value = Dense(num_actions)(hid) # build model return Model(inputs=state, outputs=q_value)
def encoder_block(input_layer, filters): x = separable_conv2d_batchnorm(input_layer, filters, strides=1, kernel=1) x = separable_conv2d_batchnorm(x, filters, strides=1, kernel=3) p1 = average_pool(x) r1 = separable_conv2d_batchnorm(input_layer, filters, strides=2, kernel=3) return layers.add([r1, p1])
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): """conv_block is the block that has a conv layer at shortcut. Arguments: input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main path filters: list of integers, the filterss of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names strides: Tuple of integers. Returns: Output tensor for the block. Note that from stage 3, the first conv layer at main path is with strides=(2,2) And the shortcut should have strides=(2,2) as well """ filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) shortcut = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor) shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) x = layers.add([x, shortcut]) x = Activation('relu')(x) return x
def res_block(init, nb_filter, k=1): x = Activation('relu')(init) x = Conv2D(nb_filter * k, (3, 3), padding='same', use_bias=False)(x) x = BN(x) x = Activation('relu')(x) x = Conv2D(nb_filter * k, (3, 3), padding='same', use_bias=False)(x) x = BN(x) x = Squeeze_excitation_layer(x) x = layers.add([init, x]) return x
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): """conv_block is the block that has a conv layer at shortcut. Arguments: input_tensor: input tensor kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the filterss of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names strides: Tuple of integers. Returns: Output tensor for the block. Note that from stage 3, the first conv layer at main path is with strides=(2,2) And the shortcut should have strides=(2,2) as well """ filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D( filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Conv2D( filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) shortcut = Conv2D( filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor) shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) x = layers.add([x, shortcut]) x = Activation('relu')(x) return x
def dw_conv(init, nb_filter, k): residual = Conv2D(nb_filter * k, (1, 1), strides=(2, 2), padding='same', use_bias=False)(init) residual = BN(residual) x = Conv2D(nb_filter * k, (3, 3), padding='same', use_bias=False)(init) x = BN(x) x = Activation('relu')(x) x = Conv2D(nb_filter * k, (3, 3), padding='same', use_bias=False)(x) x = BN(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) x = layers.add([x, residual]) return x
def identity_block(self, input, kernel, filters, stage, block): conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = input x = Conv3D(filters[0], (1, 1, 1), name=conv_name_base + '2a')(x) x = BatchNormalization(axis=4, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Conv3D(filters[1], kernel, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=4, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Conv3D(filters[2], (1, 1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=4, name=bn_name_base + '2c')(x) x = layers.add([x, input]) x = Activation('relu')(x) return x
def identity_block(input_tensor, kernel_size, filters, stage, block): """The identity block is the block that has no conv layer at shortcut. Arguments: input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main path filters: list of integers, the filterss of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names Returns: Output tensor for the block. """ filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) x = layers.add([x, input_tensor]) x = Activation('relu')(x) return x
def conv_block(self, input, kernel, filters, stage, block, strides=(2, 2, 2)): conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = input x = Conv3D(filters[0], (1, 1, 1), strides=strides, name=conv_name_base + '2a')(x) x = BatchNormalization(axis=4, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Conv3D(filters[1], kernel, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=4, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Conv3D(filters[2], (1, 1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=4, name=bn_name_base + '2c')(x) shortcut = input shortcut = Conv3D(filters[2], (1, 1, 1), strides=strides, name=conv_name_base + '1')(shortcut) shortcut = BatchNormalization(axis=3, name=bn_name_base + '1')(shortcut) x = layers.add([x, shortcut]) x = Activation('relu')(x) return x
def identity_block(input_tensor, kernel_size, filters, stage, block): """The identity block is the block that has no conv layer at shortcut. Arguments: input_tensor: input tensor kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the filterss of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names Returns: Output tensor for the block. """ filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Conv2D( filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) x = layers.add([x, input_tensor]) x = Activation('relu')(x) return x
def Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Xception architecture. Optionally loads weights pre-trained on ImageNet. This model is available for TensorFlow only, and can only be used with inputs following the TensorFlow data format `(width, height, channels)`. You should set `image_data_format="channels_last"` in your Keras config located at ~/.keras/keras.json. Note that the default input image size for this model is 299x299. Arguments: include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization) or "imagenet" (pre-training on ImageNet). 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 `(299, 299, 3)`. It should have exactly 3 input channels, and width and height should be no smaller than 71. E.g. `(150, 150, 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 layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, 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. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ if weights not in {'imagenet', None}: raise ValueError('The `weights` argument should be either ' '`None` (random initialization) or `imagenet` ' '(pre-training on ImageNet).') 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') if K.backend() != 'tensorflow': raise RuntimeError('The Xception model is only available with ' 'the TensorFlow backend.') if K.image_data_format() != 'channels_last': logging.warning( 'The Xception model is only available for the ' 'input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height). ' 'You should set `image_data_format="channels_last"` in your Keras ' 'config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None # Determine proper input shape input_shape = _obtain_input_shape(input_shape, default_size=299, min_size=71, data_format=K.image_data_format(), require_flatten=False, weights=weights) if input_tensor is None: img_input = Input(shape=input_shape) else: img_input = Input(tensor=input_tensor, shape=input_shape) x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input) x = BatchNormalization(name='block1_conv1_bn')(x) x = Activation('relu', name='block1_conv1_act')(x) x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) x = BatchNormalization(name='block1_conv2_bn')(x) x = Activation('relu', name='block1_conv2_act')(x) residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x) x = BatchNormalization(name='block2_sepconv1_bn')(x) x = Activation('relu', name='block2_sepconv2_act')(x) x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x) x = BatchNormalization(name='block2_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) x = layers.add([x, residual]) residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block3_sepconv1_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x) x = BatchNormalization(name='block3_sepconv1_bn')(x) x = Activation('relu', name='block3_sepconv2_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x) x = BatchNormalization(name='block3_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x) x = layers.add([x, residual]) residual = Conv2D(728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block4_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x) x = BatchNormalization(name='block4_sepconv1_bn')(x) x = Activation('relu', name='block4_sepconv2_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x) x = BatchNormalization(name='block4_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x) x = layers.add([x, residual]) for i in range(8): residual = x prefix = 'block' + str(i + 5) x = Activation('relu', name=prefix + '_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x) x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) x = Activation('relu', name=prefix + '_sepconv2_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x) x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) x = Activation('relu', name=prefix + '_sepconv3_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x) x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) residual = Conv2D(1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block13_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x) x = BatchNormalization(name='block13_sepconv1_bn')(x) x = Activation('relu', name='block13_sepconv2_act')(x) x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x) x = BatchNormalization(name='block13_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x) x = layers.add([x, residual]) x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x) x = BatchNormalization(name='block14_sepconv1_bn')(x) x = Activation('relu', name='block14_sepconv1_act')(x) x = SeparableConv2D(2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x) x = BatchNormalization(name='block14_sepconv2_bn')(x) x = Activation('relu', name='block14_sepconv2_act')(x) if include_top: x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = Model(inputs, x, name='xception') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'xception_weights_tf_dim_ordering_tf_kernels.h5', TF_WEIGHTS_PATH, cache_subdir='models') else: weights_path = get_file( 'xception_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models') model.load_weights(weights_path) if old_data_format: K.set_image_data_format(old_data_format) return model
def Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Xception architecture. Optionally loads weights pre-trained on ImageNet. This model is available for TensorFlow only, and can only be used with inputs following the TensorFlow data format `(width, height, channels)`. You should set `image_data_format="channels_last"` in your Keras config located at ~/.keras/keras.json. Note that the default input image size for this model is 299x299. Arguments: include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization) or "imagenet" (pre-training on ImageNet). 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 `(299, 299, 3)`. It should have exactly 3 input channels, and width and height should be no smaller than 71. E.g. `(150, 150, 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 layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, 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. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ if weights not in {'imagenet', None}: raise ValueError('The `weights` argument should be either ' '`None` (random initialization) or `imagenet` ' '(pre-training on ImageNet).') 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') if K.backend() != 'tensorflow': raise RuntimeError('The Xception model is only available with ' 'the TensorFlow backend.') if K.image_data_format() != 'channels_last': logging.warning( 'The Xception model is only available for the ' 'input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height). ' 'You should set `image_data_format="channels_last"` in your Keras ' 'config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None # Determine proper input shape input_shape = _obtain_input_shape( input_shape, default_size=299, min_size=71, data_format=K.image_data_format(), require_flatten=False, weights=weights) if input_tensor is None: img_input = Input(shape=input_shape) else: img_input = Input(tensor=input_tensor, shape=input_shape) x = Conv2D( 32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input) x = BatchNormalization(name='block1_conv1_bn')(x) x = Activation('relu', name='block1_conv1_act')(x) x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) x = BatchNormalization(name='block1_conv2_bn')(x) x = Activation('relu', name='block1_conv2_act')(x) residual = Conv2D( 128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = SeparableConv2D( 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x) x = BatchNormalization(name='block2_sepconv1_bn')(x) x = Activation('relu', name='block2_sepconv2_act')(x) x = SeparableConv2D( 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x) x = BatchNormalization(name='block2_sepconv2_bn')(x) x = MaxPooling2D( (3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) x = layers.add([x, residual]) residual = Conv2D( 256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block3_sepconv1_act')(x) x = SeparableConv2D( 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x) x = BatchNormalization(name='block3_sepconv1_bn')(x) x = Activation('relu', name='block3_sepconv2_act')(x) x = SeparableConv2D( 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x) x = BatchNormalization(name='block3_sepconv2_bn')(x) x = MaxPooling2D( (3, 3), strides=(2, 2), padding='same', name='block3_pool')(x) x = layers.add([x, residual]) residual = Conv2D( 728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block4_sepconv1_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x) x = BatchNormalization(name='block4_sepconv1_bn')(x) x = Activation('relu', name='block4_sepconv2_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x) x = BatchNormalization(name='block4_sepconv2_bn')(x) x = MaxPooling2D( (3, 3), strides=(2, 2), padding='same', name='block4_pool')(x) x = layers.add([x, residual]) for i in range(8): residual = x prefix = 'block' + str(i + 5) x = Activation('relu', name=prefix + '_sepconv1_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x) x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) x = Activation('relu', name=prefix + '_sepconv2_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x) x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) x = Activation('relu', name=prefix + '_sepconv3_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x) x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) residual = Conv2D( 1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block13_sepconv1_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x) x = BatchNormalization(name='block13_sepconv1_bn')(x) x = Activation('relu', name='block13_sepconv2_act')(x) x = SeparableConv2D( 1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x) x = BatchNormalization(name='block13_sepconv2_bn')(x) x = MaxPooling2D( (3, 3), strides=(2, 2), padding='same', name='block13_pool')(x) x = layers.add([x, residual]) x = SeparableConv2D( 1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x) x = BatchNormalization(name='block14_sepconv1_bn')(x) x = Activation('relu', name='block14_sepconv1_act')(x) x = SeparableConv2D( 2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x) x = BatchNormalization(name='block14_sepconv2_bn')(x) x = Activation('relu', name='block14_sepconv2_act')(x) if include_top: x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = Model(inputs, x, name='xception') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'xception_weights_tf_dim_ordering_tf_kernels.h5', TF_WEIGHTS_PATH, cache_subdir='models') else: weights_path = get_file( 'xception_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models') model.load_weights(weights_path) if old_data_format: K.set_image_data_format(old_data_format) return model
""" ### Residual connection on a convolution layer For more information about residual networks, see [Deep Residual Learning for Image Recognition](http://arxiv.org/abs/1512.03385). """ from tensorflow.contrib.keras.python.keras.layers import add, Input, Conv2D from tensorflow.contrib.keras.python.keras.models import Model # input tensor for a 3-channel 256x256 image # x = Input(shape=(3, 256, 256)) will cause error x = Input(shape=(256, 256, 3)) # due to keras.backend._config uses channels_last # 3x3 conv with 3 output channels (same as input channels) y = Conv2D(3, (3, 3), padding='same')(x) # this returns x + y. z = add( [x, y] ) # take in same shape input tensor list, return same shape tensor, in order words, add([x,y]) is row-bind model = Model(x, z) model.summary()
def up_conv(init, skip, nb_filter, k): x = Conv2DTranspose(nb_filter * k, (3, 3), padding='same', strides=(2, 2))(init) x = BN(x) x = layers.add([x, skip]) return x
def create_model(embeddings, config=get_config(), sentence_length=100): config['sentence_length'] = sentence_length # sentence attention attention_input = Input(shape=(config['sentence_length'], config['embedding_size'],), dtype='float32') x = Permute((2, 1))(attention_input) x = Reshape((config['embedding_size'], config['sentence_length']))(x) x = Dense(config['sentence_length'], activation='softmax', bias=True)(x) x = Lambda(lambda x: K.mean(x, axis=1), name='attention_vector_sentence')(x) x = RepeatVector(config['embedding_size'])(x) # x = Lambda(lambda x: x, name='attention_vector_sentence')(x) attention_probabilities = Permute((2, 1))(x) x = merge.multiply([attention_input, attention_probabilities], name='attention_mul') x = Lambda(lambda x: K.sum(x, axis=1))(x) sentence_attention = Model(attention_input, x, name='sentence_attention') embedding_layer = Embedding( embeddings.shape[0], embeddings.shape[1], input_length=config['sentence_length'], trainable=False, weights=[embeddings], ) pos_mbedding_layer = Embedding( 15, config['embedding_size'], input_length=config['sentence_length'], trainable=True, # weights=[embeddings], ) input = Input(shape=(config['sentence_length'],), dtype='int32', name='input_1') x = embedding_layer(input) x = SpatialDropout1D(config['embedding_dropout'])(x) x1 = Attention()(x) input2 = Input(shape=(config['sentence_length'],), dtype='int32', name='input_2') x2 = pos_mbedding_layer(input2) x2 = sentence_attention(x2) # res x_sum = Lambda(lambda x: K.mean(x, axis=1))(x) x1 = add([x1, x_sum]) x = concatenate([x1, x2]) if config['dense_layer']: x = Dense(config['dense_layer'], activation='relu')(x) x = Dropout(config['dropout_prob'])(x) output = Dense(1, activation='sigmoid',)(x) model = Model(inputs=[input, input2], outputs=output) return model, config