Python keras.layers.MaxPooling2D() Examples
The following are 30
code examples of keras.layers.MaxPooling2D().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
keras.layers
, or try the search function
.

Example #1
Source Project: ocsvm-anomaly-detection Author: hiram64 File: model.py License: MIT License | 7 votes |
def build_cae_model(height=32, width=32, channel=3): """ build convolutional autoencoder model """ input_img = Input(shape=(height, width, channel)) # encoder net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img) net = MaxPooling2D((2, 2), padding='same')(net) net = Conv2D(8, (3, 3), activation='relu', padding='same')(net) net = MaxPooling2D((2, 2), padding='same')(net) net = Conv2D(4, (3, 3), activation='relu', padding='same')(net) encoded = MaxPooling2D((2, 2), padding='same', name='enc')(net) # decoder net = Conv2D(4, (3, 3), activation='relu', padding='same')(encoded) net = UpSampling2D((2, 2))(net) net = Conv2D(8, (3, 3), activation='relu', padding='same')(net) net = UpSampling2D((2, 2))(net) net = Conv2D(16, (3, 3), activation='relu', padding='same')(net) net = UpSampling2D((2, 2))(net) decoded = Conv2D(channel, (3, 3), activation='sigmoid', padding='same')(net) return Model(input_img, decoded)
Example #2
Source Project: AI_for_Wechat_tiaoyitiao Author: lyffly File: mymodel.py License: GNU General Public License v3.0 | 7 votes |
def get_model(): model = models.Sequential() model.add(layers.Conv2D(16,(3,3),activation='relu',input_shape=(135,240,3),padding = 'same')) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(32,(3,3),activation='relu',padding = 'same')) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(64,(3,3),activation='relu',padding = 'same')) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(64,(3,3),activation='relu',padding = 'same')) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(128,(3,3),activation='relu',padding = 'same')) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Flatten()) model.add(layers.Dropout(0.5)) model.add(layers.Dense(128,activation="relu")) model.add(layers.Dropout(0.5)) model.add(layers.Dense(27,activation="softmax")) return model #model.summary() #plot_model(model, to_file='model.png')
Example #3
Source Project: dataiku-contrib Author: dataiku File: parallel_model.py License: Apache License 2.0 | 6 votes |
def build_model(x_train, num_classes): # Reset default graph. Keras leaves old ops in the graph, # which are ignored for execution but clutter graph # visualization in TensorBoard. tf.reset_default_graph() inputs = KL.Input(shape=x_train.shape[1:], name="input_image") x = KL.Conv2D(32, (3, 3), activation='relu', padding="same", name="conv1")(inputs) x = KL.Conv2D(64, (3, 3), activation='relu', padding="same", name="conv2")(x) x = KL.MaxPooling2D(pool_size=(2, 2), name="pool1")(x) x = KL.Flatten(name="flat1")(x) x = KL.Dense(128, activation='relu', name="dense1")(x) x = KL.Dense(num_classes, activation='softmax', name="dense2")(x) return KM.Model(inputs, x, "digit_classifier_model") # Load MNIST Data
Example #4
Source Project: deep_architect Author: negrinho File: keras_ops.py License: MIT License | 6 votes |
def max_pool2d(h_kernel_size, h_stride): def compile_fn(di, dh): layer = layers.MaxPooling2D(pool_size=dh['kernel_size'], strides=(dh['stride'], dh['stride']), padding='same') def fn(di): return {'out': layer(di['in'])} return fn return siso_keras_module('MaxPool2D', compile_fn, { 'kernel_size': h_kernel_size, 'stride': h_stride, })
Example #5
Source Project: n2n-watermark-remove Author: zxq2233 File: model.py License: MIT License | 6 votes |
def get_unet_model(input_channel_num=3, out_ch=3, start_ch=64, depth=4, inc_rate=2., activation='relu', dropout=0.5, batchnorm=False, maxpool=True, upconv=True, residual=False): def _conv_block(m, dim, acti, bn, res, do=0): n = Conv2D(dim, 3, activation=acti, padding='same')(m) n = BatchNormalization()(n) if bn else n n = Dropout(do)(n) if do else n n = Conv2D(dim, 3, activation=acti, padding='same')(n) n = BatchNormalization()(n) if bn else n return Concatenate()([m, n]) if res else n def _level_block(m, dim, depth, inc, acti, do, bn, mp, up, res): if depth > 0: n = _conv_block(m, dim, acti, bn, res) m = MaxPooling2D()(n) if mp else Conv2D(dim, 3, strides=2, padding='same')(n) m = _level_block(m, int(inc * dim), depth - 1, inc, acti, do, bn, mp, up, res) if up: m = UpSampling2D()(m) m = Conv2D(dim, 2, activation=acti, padding='same')(m) else: m = Conv2DTranspose(dim, 3, strides=2, activation=acti, padding='same')(m) n = Concatenate()([n, m]) m = _conv_block(n, dim, acti, bn, res) else: m = _conv_block(m, dim, acti, bn, res, do) return m i = Input(shape=(None, None, input_channel_num)) o = _level_block(i, start_ch, depth, inc_rate, activation, dropout, batchnorm, maxpool, upconv, residual) o = Conv2D(out_ch, 1)(o) model = Model(inputs=i, outputs=o) return model
Example #6
Source Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelF(): model = Sequential() model.add(Convolution2D(32, 3, 3, border_mode='valid', input_shape=(FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS, FLAGS.NUM_CHANNELS))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(64, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(1024)) model.add(Activation('relu')) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example #7
Source Project: Convolutional-Networks-for-Stock-Predicting Author: JasonDoingGreat File: cnn_main.py License: MIT License | 6 votes |
def create_model(): model = Sequential() model.add(Convolution2D(32, 3, 3, border_mode='valid', input_shape=(100, 100, 3))) model.add(Activation('relu')) model.add(Convolution2D(32, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(64, 3, 3, border_mode='valid')) model.add(Activation('relu')) model.add(Convolution2D(64, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(2)) model.add(Activation('softmax')) return model
Example #8
Source Project: Keras-DualPathNetworks Author: titu1994 File: dual_path_network.py License: Apache License 2.0 | 6 votes |
def _initial_conv_block_inception(input, initial_conv_filters, weight_decay=5e-4): ''' Adds an initial conv block, with batch norm and relu for the DPN Args: input: input tensor initial_conv_filters: number of filters for initial conv block weight_decay: weight decay factor Returns: a keras tensor ''' channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 x = Conv2D(initial_conv_filters, (7, 7), padding='same', use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay), strides=(2, 2))(input) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) return x
Example #9
Source Project: EUSIPCO2017 Author: Veleslavia File: singlelayer.py License: GNU Affero General Public License v3.0 | 5 votes |
def build_model(n_classes): if K.image_dim_ordering() == 'th': input_shape = (1, N_MEL_BANDS, SEGMENT_DUR) channel_axis = 1 else: input_shape = (N_MEL_BANDS, SEGMENT_DUR, 1) channel_axis = 3 melgram_input = Input(shape=input_shape) m_sizes = [50, 70] n_sizes = [1, 3, 5] n_filters = [128, 64, 32] maxpool_const = 4 layers = list() for m_i in m_sizes: for i, n_i in enumerate(n_sizes): x = Convolution2D(n_filters[i], m_i, n_i, border_mode='same', init='he_normal', W_regularizer=l2(1e-5), name=str(n_i)+'_'+str(m_i)+'_'+'conv')(melgram_input) x = BatchNormalization(axis=channel_axis, mode=0, name=str(n_i)+'_'+str(m_i)+'_'+'bn')(x) x = ELU()(x) x = MaxPooling2D(pool_size=(N_MEL_BANDS, SEGMENT_DUR/maxpool_const), name=str(n_i)+'_'+str(m_i)+'_'+'pool')(x) x = Flatten(name=str(n_i)+'_'+str(m_i)+'_'+'flatten')(x) layers.append(x) x = merge(layers, mode='concat', concat_axis=channel_axis) x = Dropout(0.5)(x) x = Dense(n_classes, init='he_normal', W_regularizer=l2(1e-5), activation='softmax', name='prediction')(x) model = Model(melgram_input, x) return model
Example #10
Source Project: dataiku-contrib Author: dataiku File: model.py License: Apache License 2.0 | 5 votes |
def resnet_graph(input_image, architecture, stage5=False, train_bn=True): """Build a ResNet graph. architecture: Can be resnet50 or resnet101 stage5: Boolean. If False, stage5 of the network is not created train_bn: Boolean. Train or freeze Batch Norm layers """ assert architecture in ["resnet50", "resnet101"] # Stage 1 x = KL.ZeroPadding2D((3, 3))(input_image) x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x) x = BatchNorm(name='bn_conv1')(x, training=train_bn) x = KL.Activation('relu')(x) C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x) # Stage 2 x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn) C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn) # Stage 3 x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn) x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn) x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn) C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn) # Stage 4 x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn) block_count = {"resnet50": 5, "resnet101": 22}[architecture] for i in range(block_count): x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn) C4 = x # Stage 5 if stage5: x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn) x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn) C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn) else: C5 = None return [C1, C2, C3, C4, C5] ############################################################ # Proposal Layer ############################################################
Example #11
Source Project: MesoNet Author: DariusAf File: classifiers.py License: Apache License 2.0 | 5 votes |
def init_model(self, dl_rate): x = Input(shape = (IMGWIDTH, IMGWIDTH, 3)) x1 = Conv2D(16, (3, 3), dilation_rate = dl_rate, strides = 1, padding='same', activation = 'relu')(x) x1 = Conv2D(4, (1, 1), padding='same', activation = 'relu')(x1) x1 = BatchNormalization()(x1) x1 = MaxPooling2D(pool_size=(8, 8), padding='same')(x1) y = Flatten()(x1) y = Dropout(0.5)(y) y = Dense(1, activation = 'sigmoid')(y) return KerasModel(inputs = x, outputs = y)
Example #12
Source Project: MesoNet Author: DariusAf File: classifiers.py License: Apache License 2.0 | 5 votes |
def init_model(self): x = Input(shape = (IMGWIDTH, IMGWIDTH, 3)) x1 = Conv2D(8, (3, 3), padding='same', activation = 'relu')(x) x1 = BatchNormalization()(x1) x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1) x2 = Conv2D(8, (5, 5), padding='same', activation = 'relu')(x1) x2 = BatchNormalization()(x2) x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2) x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2) x3 = BatchNormalization()(x3) x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3) x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3) x4 = BatchNormalization()(x4) x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4) y = Flatten()(x4) y = Dropout(0.5)(y) y = Dense(16)(y) y = LeakyReLU(alpha=0.1)(y) y = Dropout(0.5)(y) y = Dense(1, activation = 'sigmoid')(y) return KerasModel(inputs = x, outputs = y)
Example #13
Source Project: MesoNet Author: DariusAf File: classifiers.py License: Apache License 2.0 | 5 votes |
def init_model(self): x = Input(shape = (IMGWIDTH, IMGWIDTH, 3)) x1 = self.InceptionLayer(1, 4, 4, 2)(x) x1 = BatchNormalization()(x1) x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1) x2 = self.InceptionLayer(2, 4, 4, 2)(x1) x2 = BatchNormalization()(x2) x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2) x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2) x3 = BatchNormalization()(x3) x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3) x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3) x4 = BatchNormalization()(x4) x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4) y = Flatten()(x4) y = Dropout(0.5)(y) y = Dense(16)(y) y = LeakyReLU(alpha=0.1)(y) y = Dropout(0.5)(y) y = Dense(1, activation = 'sigmoid')(y) return KerasModel(inputs = x, outputs = y)
Example #14
Source Project: backdoor Author: bolunwang File: gtsrb_injection_example.py License: MIT License | 5 votes |
def load_traffic_sign_model(base=32, dense=512, num_classes=43): input_shape = (32, 32, 3) model = Sequential() model.add(Conv2D(base, (3, 3), padding='same', input_shape=input_shape, activation='relu')) model.add(Conv2D(base, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Conv2D(base * 2, (3, 3), padding='same', activation='relu')) model.add(Conv2D(base * 2, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Conv2D(base * 4, (3, 3), padding='same', activation='relu')) model.add(Conv2D(base * 4, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Flatten()) model.add(Dense(dense, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) opt = keras.optimizers.adam(lr=0.001, decay=1 * 10e-5) model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) return model
Example #15
Source Project: PanopticSegmentation Author: dmechea File: model.py License: MIT License | 5 votes |
def resnet_graph(input_image, architecture, stage5=False, train_bn=True): """Build a ResNet graph. architecture: Can be resnet50 or resnet101 stage5: Boolean. If False, stage5 of the network is not created train_bn: Boolean. Train or freeze Batch Norm layers """ assert architecture in ["resnet50", "resnet101"] # Stage 1 x = KL.ZeroPadding2D((3, 3))(input_image) x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x) x = BatchNorm(name='bn_conv1')(x, training=train_bn) x = KL.Activation('relu')(x) C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x) # Stage 2 x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn) C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn) # Stage 3 x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn) x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn) x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn) C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn) # Stage 4 x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn) block_count = {"resnet50": 5, "resnet101": 22}[architecture] for i in range(block_count): x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn) C4 = x # Stage 5 if stage5: x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn) x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn) C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn) else: C5 = None return [C1, C2, C3, C4, C5] ############################################################ # Proposal Layer ############################################################
Example #16
Source Project: PanopticSegmentation Author: dmechea File: parallel_model.py License: MIT License | 5 votes |
def build_model(x_train, num_classes): # Reset default graph. Keras leaves old ops in the graph, # which are ignored for execution but clutter graph # visualization in TensorBoard. tf.reset_default_graph() inputs = KL.Input(shape=x_train.shape[1:], name="input_image") x = KL.Conv2D(32, (3, 3), activation='relu', padding="same", name="conv1")(inputs) x = KL.Conv2D(64, (3, 3), activation='relu', padding="same", name="conv2")(x) x = KL.MaxPooling2D(pool_size=(2, 2), name="pool1")(x) x = KL.Flatten(name="flat1")(x) x = KL.Dense(128, activation='relu', name="dense1")(x) x = KL.Dense(num_classes, activation='softmax', name="dense2")(x) return KM.Model(inputs, x, "digit_classifier_model") # Load MNIST Data
Example #17
Source Project: stagesepx Author: williamfzc File: keras.py License: MIT License | 5 votes |
def create_model(self) -> Sequential: """ model structure. you can overwrite this method to build your own model """ logger.info(f"creating keras sequential model") if K.image_data_format() == "channels_first": input_shape = (1, *self.data_size) else: input_shape = (*self.data_size, 1) model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=input_shape)) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(32, (3, 3))) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (3, 3))) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(64)) model.add(Activation("relu")) model.add(Dropout(0.5)) model.add(Dense(6)) model.add(Activation("softmax")) model.compile( loss="sparse_categorical_crossentropy", optimizer="rmsprop", metrics=["accuracy"], ) logger.info("model created") return model
Example #18
Source Project: Dropout_BBalpha Author: YingzhenLi File: BBalpha_dropout.py License: MIT License | 5 votes |
def get_logit_cnn_layers(nb_units, p, wd, nb_classes, layers = [], dropout = False): # number of convolutional filters to use nb_filters = 32 # size of pooling area for max pooling pool_size = (2, 2) # convolution kernel size kernel_size = (3, 3) if dropout == 'MC': D = Dropout_mc if dropout == 'pW': D = pW if dropout == 'none': D = Identity layers.append(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], border_mode='valid', W_regularizer=l2(wd))) layers.append(Activation('relu')) layers.append(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], W_regularizer=l2(wd))) layers.append(Activation('relu')) layers.append(MaxPooling2D(pool_size=pool_size)) layers.append(Flatten()) layers.append(D(p)) layers.append(Dense(nb_units, W_regularizer=l2(wd))) layers.append(Activation('relu')) layers.append(D(p)) layers.append(Dense(nb_classes, W_regularizer=l2(wd))) return layers
Example #19
Source Project: face_landmark_dnn Author: junhwanjang File: train_basic_models.py License: MIT License | 5 votes |
def facial_landmark_cnn(input_shape=INPUT_SHAPE, output_size=OUTPUT_SIZE): # Stage 1 # img_input = Input(shape=input_shape) ## Block 1 ## x = Conv2D(32, (3,3), strides=(1,1), name='S1_conv1')(img_input) x = BatchNormalization()(x) x = Activation('relu', name='S1_relu_conv1')(x) x = MaxPooling2D(pool_size=(2,2), strides=(2,2), name='S1_pool1')(x) ## Block 2 ## x = Conv2D(64, (3,3), strides=(1,1), name='S1_conv2')(x) x = BatchNormalization()(x) x = Activation('relu', name='S1_relu_conv2')(x) x = Conv2D(64, (3,3), strides=(1,1), name='S1_conv3')(x) x = BatchNormalization()(x) x = Activation('relu', name='S1_relu_conv3')(x) x = MaxPooling2D(pool_size=(2,2), strides=(2,2), name='S1_pool2')(x) ## Block 3 ## x = Conv2D(64, (3,3), strides=(1,1), name='S1_conv4')(x) x = BatchNormalization()(x) x = Activation('relu', name='S1_relu_conv4')(x) x = Conv2D(64, (3,3), strides=(1,1), name='S1_conv5')(x) x = BatchNormalization()(x) x = Activation('relu', name='S1_relu_conv5')(x) x = MaxPooling2D(pool_size=(2,2), strides=(2,2), name='S1_pool3')(x) ## Block 4 ## x = Conv2D(256, (3,3), strides=(1,1), name='S1_conv8')(x) x = BatchNormalization()(x) x = Activation('relu', name='S1_relu_conv8')(x) x = Dropout(0.2)(x) ## Block 5 ## x = Flatten(name='S1_flatten')(x) x = Dense(2048, activation='relu', name='S1_fc1')(x) x = Dense(output_size, activation=None, name='S1_predictions')(x) model = Model([img_input], x, name='facial_landmark_model') return model
Example #20
Source Project: MCF-3D-CNN Author: xyj77 File: liver_model.py License: MIT License | 5 votes |
def cnn_2D(self, input_shape, modual=''): #建立Sequential模型 model_in = Input(input_shape) model = Conv2D( filters = 6, kernel_size = (3, 3), input_shape = input_shape, activation='relu', kernel_initializer='he_normal', name = modual+'conv1' )(model_in)# now 30x30x6 model = MaxPooling2D(pool_size=(2,2))(model)# now 15x15x6 model = Conv2D( filters = 8, kernel_size = (4, 4), activation='relu', kernel_initializer='he_normal', name = modual+'conv2' )(model)# now 12x12x8 model = MaxPooling2D(pool_size=(2,2))(model)# now 6x6x8 model = Flatten()(model) model = Dropout(0.5)(model) model_out = Dense(100, activation='relu', name = modual+'fc1')(model) return model_in, model_out
Example #21
Source Project: robust_physical_perturbations Author: evtimovi File: model.py License: MIT License | 5 votes |
def cunn_keras(img_rows=FLAGS.img_rows, img_cols=FLAGS.img_cols, channels=FLAGS.nb_channels, nb_classes=FLAGS.nb_classes): ''' Defines the VGG 16 model using the Keras Sequential model :param img_rows: number of row in the image :param img_cols: number of columns in the image :param channels: number of color channels (e.g., 1 for MNIST) :param nb_classes: the number of output classes :return: a Keras model. Call with model(<input_tensor>) ''' input = Input(shape=(img_rows, img_cols, channels)) conv1 = Convolution2D(32,5,5, border_mode='same', subsample=(1,1), activation='relu')(input) pool1 = MaxPooling2D((2,2), strides=(2,2))(conv1) conv2 = Convolution2D(64,5,5, border_mode='same', subsample=(1,1), activation='relu')(pool1) pool2 = MaxPooling2D((2,2), strides=(2,2))(conv2) conv3 = Convolution2D(128,5,5, border_mode='same', subsample=(1,1), activation='relu')(pool2) pool3 = MaxPooling2D((2,2), strides=(2,2))(conv3) flat1 = Flatten()(pool1) flat2 = Flatten()(pool2) flat3 = Flatten()(pool3) flat_all = merge([flat1, flat2, flat3], mode='concat', concat_axis=1) #If this gives an error, update the keras tensorflow backend. It is likely that is making the call tf.concat(axis, [to_dense(x) for x in tensors]) in of tf.concat([to_dense(x) for x in tensors], axis) fc = Dense(1024)(flat_all) drop = Dropout(0.5)(fc) fc2 = Dense(nb_classes)(drop) output = Activation('softmax',name='prob')(fc2) model = Model(input=input, output=output) return model
Example #22
Source Project: udacity-SDC-baseline Author: dolaameng File: model.py License: MIT License | 5 votes |
def build_cnn(image_size=None): image_size = image_size or (60, 80) if K.image_dim_ordering() == 'th': input_shape = (3,) + image_size else: input_shape = image_size + (3, ) img_input = Input(input_shape) x = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(img_input) x = Dropout(0.5)(x) x = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(x) x = Dropout(0.5)(x) x = MaxPooling2D((2, 2), strides=(2, 2))(x) x = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(x) x = Dropout(0.5)(x) # it doesn't fit in my GPU # x = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(x) # x = Dropout(0.5)(x) x = MaxPooling2D((2, 2), strides=(2, 2))(x) y = Flatten()(x) y = Dense(1024, activation='relu')(y) y = Dropout(.5)(y) y = Dense(1024, activation='relu')(y) y = Dropout(.5)(y) y = Dense(1)(y) model = Model(input=img_input, output=y) model.compile(optimizer=Adam(lr=1e-4), loss = 'mse') return model
Example #23
Source Project: DigiEncoder Author: akshaybahadur21 File: Coder.py License: MIT License | 5 votes |
def encoder(self): encoded = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img_conv) encoded = MaxPooling2D((2, 2), padding='same')(encoded) encoded = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded) encoded = MaxPooling2D((2, 2), padding='same')(encoded) encoded = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded) encoded = MaxPooling2D((2, 2), padding='same')(encoded) return encoded
Example #24
Source Project: MassImageRetrieval Author: liuguiyangnwpu File: SiameseModel.py License: Apache License 2.0 | 5 votes |
def get_Shared_Model(input_dim): sharedNet = Sequential() sharedNet.add(Dense(128, input_shape=(input_dim,), activation='relu')) sharedNet.add(Dropout(0.1)) sharedNet.add(Dense(128, activation='relu')) sharedNet.add(Dropout(0.1)) sharedNet.add(Dense(128, activation='relu')) # sharedNet.add(Dropout(0.1)) # sharedNet.add(Dense(3, activation='relu')) # sharedNet = Sequential() # sharedNet.add(Dense(4096, activation="tanh", kernel_regularizer=l2(2e-3))) # sharedNet.add(Reshape(target_shape=(64, 64, 1))) # sharedNet.add(Conv2D(filters=64, kernel_size=3, strides=(2, 2), padding="same", activation="relu", kernel_regularizer=l2(1e-3))) # sharedNet.add(MaxPooling2D()) # sharedNet.add(Conv2D(filters=128, kernel_size=3, strides=(2, 2), padding="same", activation="relu", kernel_regularizer=l2(1e-3))) # sharedNet.add(MaxPooling2D()) # sharedNet.add(Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding="same", activation="relu", kernel_regularizer=l2(1e-3))) # sharedNet.add(Flatten()) # sharedNet.add(Dense(1024, activation="sigmoid", kernel_regularizer=l2(1e-3))) return sharedNet
Example #25
Source Project: head-detection-using-yolo Author: pranoyr File: backend.py License: MIT License | 5 votes |
def __init__(self, input_size): input_image = Input(shape=(input_size, input_size, 3)) # Layer 1 x = Conv2D(16, (3,3), strides=(1,1), padding='same', name='conv_1', use_bias=False)(input_image) x = BatchNormalization(name='norm_1')(x) x = LeakyReLU(alpha=0.1)(x) x = MaxPooling2D(pool_size=(2, 2))(x) # Layer 2 - 5 for i in range(0,4): x = Conv2D(32*(2**i), (3,3), strides=(1,1), padding='same', name='conv_' + str(i+2), use_bias=False)(x) x = BatchNormalization(name='norm_' + str(i+2))(x) x = LeakyReLU(alpha=0.1)(x) x = MaxPooling2D(pool_size=(2, 2))(x) # Layer 6 x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_6', use_bias=False)(x) x = BatchNormalization(name='norm_6')(x) x = LeakyReLU(alpha=0.1)(x) x = MaxPooling2D(pool_size=(2, 2), strides=(1,1), padding='same')(x) # Layer 7 - 8 for i in range(0,2): x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_' + str(i+7), use_bias=False)(x) x = BatchNormalization(name='norm_' + str(i+7))(x) x = LeakyReLU(alpha=0.1)(x) self.feature_extractor = Model(input_image, x) self.feature_extractor.load_weights(TINY_YOLO_BACKEND_PATH)
Example #26
Source Project: convnet-drawer Author: yu4u File: AlexNet.py License: MIT License | 5 votes |
def get_model(): model = Sequential() model.add(Conv2D(96, kernel_size=(11, 11), strides=(4, 4), input_shape=(227, 227, 3))) model.add(MaxPooling2D((3, 3), strides=(2, 2))) model.add(Conv2D(256, (5, 5), padding="same")) model.add(MaxPooling2D((3, 3), strides=(2, 2))) model.add(Conv2D(384, (3, 3), padding="same")) model.add(Conv2D(384, (3, 3), padding="same")) model.add(Conv2D(256, (3, 3), padding="same")) model.add(MaxPooling2D((3, 3), strides=(2, 2))) model.add(Flatten()) model.add(Dense(4096)) model.add(Dense(4096)) model.add(Dense(1000)) return model
Example #27
Source Project: Keras-Project-Template Author: Ahmkel File: conv_mnist_model.py License: Apache License 2.0 | 5 votes |
def build_model(self): self.model = Sequential() self.model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1))) self.model.add(Conv2D(64, (3, 3), activation='relu')) self.model.add(MaxPooling2D(pool_size=(2, 2))) self.model.add(Dropout(0.25)) self.model.add(Flatten()) self.model.add(Dense(128, activation='relu')) self.model.add(Dropout(0.5)) self.model.add(Dense(10, activation='softmax')) self.model.compile( loss='sparse_categorical_crossentropy', optimizer=self.config.model.optimizer, metrics=['accuracy'])
Example #28
Source Project: EasyPR-python Author: SunskyF File: model.py License: Apache License 2.0 | 5 votes |
def resnet_graph(input_image, architecture, stage5=False): assert architecture in ["resnet50", "resnet101"] # Stage 1 x = KL.ZeroPadding2D((3, 3))(input_image) x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x) x = BatchNorm(axis=3, name='bn_conv1')(x) x = KL.Activation('relu')(x) C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x) # Stage 2 x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') # Stage 3 x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') # Stage 4 x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') block_count = {"resnet50": 5, "resnet101": 22}[architecture] for i in range(block_count): x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i)) C4 = x # Stage 5 if stage5: x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') else: C5 = None return [C1, C2, C3, C4, C5] ############################################################ # Proposal Layer ############################################################
Example #29
Source Project: Generative-Adversarial-Networks-Cookbook Author: PacktPublishing File: discriminator.py License: MIT License | 5 votes |
def model(self): input_layer = Input(shape=self.SHAPE) x = Convolution2D(96,3,3, subsample=(2,2), border_mode='same',activation='relu')(input_layer) x = Convolution2D(64,3,3, subsample=(2,2), border_mode='same',activation='relu')(x) x = MaxPooling2D(pool_size=(3,3),border_mode='same')(x) x = Convolution2D(32,3,3, subsample=(1,1), border_mode='same',activation='relu')(x) x = Convolution2D(32,1,1, subsample=(1,1), border_mode='same',activation='relu')(x) x = Convolution2D(2,1,1, subsample=(1,1), border_mode='same',activation='relu')(x) output_layer = Reshape((-1,2))(x) return Model(input_layer,output_layer)
Example #30
Source Project: WannaPark Author: dalmia File: train_detection.py License: GNU General Public License v3.0 | 5 votes |
def VGG_16(): '''Model definition''' model = Sequential() model.add(Conv2D(64, (11, 11,), padding='valid', strides=(4,4), input_shape=(img_height,img_width,num_channels), name='conv1')) model.add(Activation('relu', name='relu1')) model.add(LocalResponseNormalization(name='norm1')) model.add(MaxPooling2D((2,2), padding='same', name='pool1')) model.add(Conv2D(256, (5,5), padding='same', name='conv2')) model.add(Activation('relu', name='relu2')) model.add(LocalResponseNormalization(name='norm2')) model.add(MaxPooling2D((2,2), padding='same', name='pool2')) model.add(Conv2D(256, (3, 3), padding='same', name='conv3')) model.add(Activation('relu', name='relu3')) model.add(Conv2D(256, (3, 3), padding='same', name='conv4')) model.add(Activation('relu', name='relu4')) model.add(Conv2D(256, (3, 3), padding='same', name='conv5')) model.add(Activation('relu', name='relu5')) model.add(MaxPooling2D((2,2), padding='same', name='pool5')) return model