我们从Python开源项目中，提取了以下38个代码示例，用于说明如何使用keras.layers.Convolution3D()。
def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1): subsample = (subsample_factor, subsample_factor, subsample_factor) x = BatchNormalization(axis=4)(input_tensor) x = Activation('relu')(x) x = Convolution3D(nb_filters, 3, 3, 3, subsample=subsample, border_mode='same')(x) x = BatchNormalization(axis=4)(x) x = Activation('relu')(x) x = Convolution3D(nb_filters, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(x) if subsample_factor > 1: shortcut = Convolution3D(nb_filters, 1, 1, 1, subsample=subsample, border_mode='same')(input_tensor) else: shortcut = input_tensor x = merge([x, shortcut], mode='sum') return x
def create_cnn_network(input_dim, no_conv_filt, dense_n): '''Base network to be shared (eq. to feature extraction). ''' seq = Sequential() kern_size = 3 # conv layers seq.add(Convolution3D(no_conv_filt, kern_size, kern_size, kern_size, input_shape=input_dim, border_mode='valid', dim_ordering='th', activation='relu')) seq.add(Dropout(.1)) seq.add(BatchNormalization(mode=2)) # dense layers seq.add(Flatten()) seq.add(Dense(dense_n, activation='relu')) seq.add(BatchNormalization(mode=2)) return seq # a network with a couple dense layers
def create_cnn_network(input_dim, no_conv_filt, dense_n): '''Base network to be shared (eq. to feature extraction). ''' seq = Sequential() kern_size = 3 # conv layer seq.add(Convolution3D(no_conv_filt, kern_size, kern_size, kern_size, input_shape=input_dim, border_mode='valid', dim_ordering='th', activation='relu')) #seq.add(Dropout(.1)) seq.add(BatchNormalization(mode=2)) # dense layer seq.add(Flatten()) seq.add(Dense(dense_n, activation='relu')) seq.add(BatchNormalization(mode=2)) return seq # a CNN layer for intensity inputs
def create_cnn_network_small(input_dim, no_conv_filt, dense_n): '''Base network to be shared (eq. to feature extraction). ''' seq = Sequential() kern_size = 3 # conv layer seq.add(Convolution3D(no_conv_filt, kern_size, kern_size, kern_size, input_shape=input_dim, border_mode='valid', dim_ordering='th', activation='relu')) seq.add(Dropout(.2)) seq.add(BatchNormalization(mode=2)) # dense layer seq.add(Flatten()) seq.add(Dense(dense_n, activation='relu')) seq.add(Dropout(.2)) seq.add(BatchNormalization(mode=2)) return seq # train model given x_train and y_train
def unet_model_xd3_2_6l_grid(nb_filter=48, dim=5, clen=3 , img_rows=224, img_cols=224 ): # NOTE that this procedure is/should be used with img_rows & img_cols as None # aiming for architecture similar to the http://cs231n.stanford.edu/reports2016/317_Report.pdf # Our model is six layers deep, consisting of a series of three CONV-RELU-POOL layyers (with 32, 32, and 64 3x3 filters), a CONV-RELU layer (with 128 3x3 filters), three UPSCALE-CONV-RELU lay- ers (with 64, 32, and 32 3x3 filters), and a final 1x1 CONV- SIGMOID layer to output pixel-level predictions. Its struc- ture resembles Figure 2, though with the number of pixels, filters, and levels as described here ## 3D CNN version of a previously developed unet_model_xd_6j zconv = clen inputs = Input((1, dim, img_rows, img_cols)) conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(inputs) conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool1) conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool2) conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv4) up6 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv2], mode='concat', concat_axis=1) conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up6) conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv6) up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1) # original - only works for even dim conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up7) conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv7) pool11 = MaxPooling3D(pool_size=(2, 1, 1))(conv7) conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool11) conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv12) pool12 = MaxPooling3D(pool_size=(2, 1, 1))(conv12) conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool12) conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv13) pool13 = MaxPooling3D(pool_size=(2, 1, 1))(conv13) if (dim < 16): conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool13) else: # need one extra layer to get to 1D x 2D mask ... conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool13) conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv14) pool14 = MaxPooling3D(pool_size=(2, 1, 1))(conv14) conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool14) model = Model(input=inputs, output=conv8) model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef]) #model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), loss=dice_coef_loss, metrics=[dice_coef]) return model
def get_net(input_shape=(CUBE_SIZE, CUBE_SIZE, CUBE_SIZE, 1), load_weight_path=None, features=False, mal=False) -> Model: inputs = Input(shape=input_shape, name="input_1") x = inputs #x = AveragePooling3D(pool_size=(2, 1, 1), strides=(2, 1, 1), border_mode="same")(x) x = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')(x) # 2nd layer group x = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')(x) #if USE_DROPOUT: # x = Dropout(p=0.3)(x) # 3rd layer group x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a', subsample=(1, 1, 1))(x) x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')(x) #if USE_DROPOUT: # x = Dropout(p=0.4)(x) # 4th layer group x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a', subsample=(1, 1, 1))(x) x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b', subsample=(1, 1, 1),)(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')(x) #if USE_DROPOUT: # x = Dropout(p=0.5)(x) last64 = Convolution3D(64, 2, 2, 2, activation="relu", name="last_64")(x) out_class = Convolution3D(1, 1, 1, 1, activation="sigmoid", name="out_class_last")(last64) out_class = Flatten(name="out_class")(out_class) out_malignancy = Convolution3D(1, 1, 1, 1, activation=None, name="out_malignancy_last")(last64) out_malignancy = Flatten(name="out_malignancy")(out_malignancy) model = Model(input=inputs, output=[out_class, out_malignancy]) if load_weight_path is not None: model.load_weights(load_weight_path, by_name=False) #model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy", "out_malignancy": mean_absolute_error}, metrics={"out_class": [binary_accuracy, binary_crossentropy], "out_malignancy": mean_absolute_error}) model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy"}, metrics={"out_class": [binary_accuracy, binary_crossentropy]}) if features: model = Model(input=inputs, output=[last64]) model.summary(line_length=140) return model
def define_model(image_shape): img_input = Input(shape=image_shape) x = Convolution3D(16, 5, 5, 5, subsample=(1, 1, 1), border_mode='same')(img_input) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=32, block=1, subsample_factor=2) x = res_block(x, nb_filters=32, block=1, subsample_factor=1) x = res_block(x, nb_filters=32, block=1, subsample_factor=1) x = res_block(x, nb_filters=64, block=2, subsample_factor=2) x = res_block(x, nb_filters=64, block=2, subsample_factor=1) x = res_block(x, nb_filters=64, block=2, subsample_factor=1) x = res_block(x, nb_filters=128, block=3, subsample_factor=2) x = res_block(x, nb_filters=128, block=3, subsample_factor=1) x = res_block(x, nb_filters=128, block=3, subsample_factor=1) x = BatchNormalization(axis=4)(x) x = Activation('relu')(x) x = AveragePooling3D(pool_size=(4, 4, 8))(x) x = Flatten()(x) x = Dense(1, activation='sigmoid', name='predictions')(x) model = Model(img_input, x) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', 'precision', 'recall', 'fmeasure']) model.summary() return model
def define_model(image_shape): img_input = Input(shape=image_shape) x = Convolution3D(16, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(img_input) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=32, block=1, subsample_factor=2) x = res_block(x, nb_filters=32, block=1, subsample_factor=1) x = res_block(x, nb_filters=32, block=1, subsample_factor=1) x = res_block(x, nb_filters=64, block=2, subsample_factor=2) x = res_block(x, nb_filters=64, block=2, subsample_factor=1) x = res_block(x, nb_filters=64, block=2, subsample_factor=1) x = res_block(x, nb_filters=64, block=2, subsample_factor=1) x = res_block(x, nb_filters=128, block=3, subsample_factor=2) x = res_block(x, nb_filters=128, block=3, subsample_factor=1) x = res_block(x, nb_filters=128, block=3, subsample_factor=1) x = res_block(x, nb_filters=128, block=3, subsample_factor=1) x = res_block(x, nb_filters=256, block=4, subsample_factor=2) x = res_block(x, nb_filters=256, block=4, subsample_factor=1) x = res_block(x, nb_filters=256, block=4, subsample_factor=1) x = res_block(x, nb_filters=256, block=4, subsample_factor=1) x = BatchNormalization(axis=4)(x) x = Activation('relu')(x) x = AveragePooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2), border_mode='valid')(x) x = Flatten()(x) x = Dense(1, activation='sigmoid', name='predictions')(x) model = Model(img_input, x) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', 'precision', 'recall', 'fmeasure']) model.summary() return model
def define_model(): img_input = Input(shape=(32, 32, 64, 1)) x = Convolution3D(16, 5, 5, 5, subsample=(1, 1, 1), border_mode='same')(img_input) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=16, block=0, subsample_factor=1) x = res_block(x, nb_filters=32, block=1, subsample_factor=2) x = res_block(x, nb_filters=32, block=1, subsample_factor=1) x = res_block(x, nb_filters=32, block=1, subsample_factor=1) x = res_block(x, nb_filters=64, block=2, subsample_factor=2) x = res_block(x, nb_filters=64, block=2, subsample_factor=1) x = res_block(x, nb_filters=64, block=2, subsample_factor=1) x = res_block(x, nb_filters=128, block=3, subsample_factor=2) x = res_block(x, nb_filters=128, block=3, subsample_factor=1) x = res_block(x, nb_filters=128, block=3, subsample_factor=1) x = BatchNormalization(axis=4)(x) x = Activation('relu')(x) x = AveragePooling3D(pool_size=(4, 4, 8))(x) x = Flatten()(x) x = Dense(1, activation='sigmoid', name='predictions')(x) model = Model(img_input, x) model.compile(optimizer='adam', loss='binary_crossentropy') return model
def get_net(input_shape=(CUBE_SIZE, CUBE_SIZE, CUBE_SIZE, 1), load_weight_path=None, features=False, mal=False) -> Model: inputs = Input(shape=input_shape, name="input_1") x = inputs x = AveragePooling3D(pool_size=(2, 1, 1), strides=(2, 1, 1), border_mode="same")(x) x = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')(x) # 2nd layer group x = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')(x) if USE_DROPOUT: x = Dropout(p=0.3)(x) # 3rd layer group x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a', subsample=(1, 1, 1))(x) x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')(x) if USE_DROPOUT: x = Dropout(p=0.4)(x) # 4th layer group x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a', subsample=(1, 1, 1))(x) x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b', subsample=(1, 1, 1),)(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')(x) if USE_DROPOUT: x = Dropout(p=0.5)(x) last64 = Convolution3D(64, 2, 2, 2, activation="relu", name="last_64")(x) out_class = Convolution3D(1, 1, 1, 1, activation="sigmoid", name="out_class_last")(last64) out_class = Flatten(name="out_class")(out_class) out_malignancy = Convolution3D(1, 1, 1, 1, activation=None, name="out_malignancy_last")(last64) out_malignancy = Flatten(name="out_malignancy")(out_malignancy) model = Model(input=inputs, output=[out_class, out_malignancy]) if load_weight_path is not None: model.load_weights(load_weight_path, by_name=False) model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy", "out_malignancy": mean_absolute_error}, metrics={"out_class": [binary_accuracy, binary_crossentropy], "out_malignancy": mean_absolute_error}) if features: model = Model(input=inputs, output=[last64]) model.summary(line_length=140) return model
def create_cnn_network(input_dim): '''Base network to be shared (eq. to feature extraction). ''' seq = Sequential() nb_filter = [12, 6] kern_size = 3 # conv layers seq.add(Convolution3D(nb_filter[0], kern_size, kern_size, kern_size, input_shape=input_dim, border_mode='valid', dim_ordering='th', activation='relu')) # seq.add(MaxPooling3D(pool_size=(2, 2, 2))) # downsample seq.add(Dropout(.25)) # conv layer 2 # seq.add(Convolution3D(nb_filter[1], kern_size, kern_size, kern_size, border_mode='valid', dim_ordering='th', # activation='relu')) # # seq.add(MaxPooling3D(pool_size=(2, 2, 2), dim_ordering='th')) # downsample # seq.add(Dropout(.25)) # dense layers seq.add(Flatten()) seq.add(Dense(100, activation='relu')) seq.add(Dropout(0.2)) seq.add(Dense(50, activation='relu')) return seq # load data
def create_cnn_network(input_dim): '''Base network to be shared (eq. to feature extraction). ''' seq = Sequential() nb_filter = [12, 6] kern_size = 3 # conv layers seq.add(Convolution3D(nb_filter[0], kern_size, kern_size, kern_size, input_shape=input_dim, border_mode='valid', dim_ordering='th', activation='relu')) # seq.add(MaxPooling3D(pool_size=(2, 2, 2))) # downsample seq.add(Dropout(.25)) # conv layer 2 seq.add(Convolution3D(nb_filter[1], kern_size, kern_size, kern_size, border_mode='same', dim_ordering='th', activation='relu')) # seq.add(MaxPooling3D(pool_size=(2, 2, 2), dim_ordering='th')) # downsample seq.add(Dropout(.25)) # dense layers seq.add(Flatten()) seq.add(Dense(100, activation='relu')) seq.add(Dropout(0.1)) seq.add(Dense(50, activation='relu')) return seq # load data
def create_cnn_network(input_dim): '''Base network to be shared (eq. to feature extraction). ''' seq = Sequential() # conv layers kern_size = 3 seq.add(Convolution3D(5, kern_size, kern_size, kern_size, input_shape=input_dim, border_mode='valid', dim_ordering='th', activation='relu')) seq.add(Dropout(.25)) seq.add(BatchNormalization(mode=2)) kern_size = 3 seq.add(Convolution3D(15, kern_size, kern_size, kern_size, border_mode='valid', dim_ordering='th', activation='relu')) seq.add(Dropout(.25)) seq.add(BatchNormalization(mode=2)) # dense layers seq.add(Flatten()) seq.add(Dense(50, activation='relu')) seq.add(Dropout(.25)) seq.add(BatchNormalization(mode=2)) return seq # load data
def build(video_shape, audio_spectrogram_size): model = Sequential() model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero1', input_shape=video_shape)) model.add(Convolution3D(32, (3, 5, 5), strides=(1, 2, 2), kernel_initializer='he_normal', name='conv1')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max1')) model.add(Dropout(0.25)) model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero2')) model.add(Convolution3D(64, (3, 5, 5), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv2')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max2')) model.add(Dropout(0.25)) model.add(ZeroPadding3D(padding=(1, 1, 1), name='zero3')) model.add(Convolution3D(128, (3, 3, 3), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv3')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max3')) model.add(Dropout(0.25)) model.add(TimeDistributed(Flatten(), name='time')) model.add(Dense(1024, kernel_initializer='he_normal', name='dense1')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(1024, kernel_initializer='he_normal', name='dense2')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(2048, kernel_initializer='he_normal', name='dense3')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(2048, kernel_initializer='he_normal', name='dense4')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(audio_spectrogram_size, name='output')) model.summary() return VideoToSpeechNet(model)
def ecog_3d_model(channels=None, weights=None): input_tensor = Input(shape=(1,8,8, 1000)) # Block 1 x = AveragePooling3D((1, 1, 5), name='pre_pool')(input_tensor) x = Convolution3D(4, 2, 2, 3, border_mode='same', name='block1_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D((2, 2, 3), name='block1_pool')(x) # Block 2 x = Convolution3D(8, 2, 2, 3, border_mode='same', name='block2_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D(( 1, 1, 3), name='block2_pool')(x) # Block 3 x = Convolution3D(16, 2,2, 3, border_mode='same', name='block3_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D((1, 1, 2), name='block3_pool')(x) # Block 4 # x = Convolution2D(32, 1, 3, border_mode='same', name='block4_conv1')(x) # x = BatchNormalization(axis=1)(x) # x = Activation('relu')(x) # x = MaxPooling2D((1, 2), name='block4_pool')(x) x = Flatten(name='flatten')(x) x = Dropout(0.5)(x) x = Dense(64, W_regularizer=l2(0.01), name='fc1')(x) #x = BatchNormalization()(x) #x = Activation('relu')(x) #x = Dropout(0.5)(x) #x = Dense(1, name='predictions')(x) # x = BatchNormalization()(x) predictions = Activation('sigmoid')(x) # for layer in base_model.layers[:10]: # layer.trainable = False model = Model(input=input_tensor, output=predictions) if weights is not None: model.load_weights(weights) return model
