自学教程：Python layers.Convolution3D方法代码示例

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]def conv_block(x, nb_filter, nb0, nb1, nb2, border_mode='same', subsample=(1, 1, 1), bias=True, batch_norm=False):        if K.image_dim_ordering() == "th":        channel_axis = 1    else:        channel_axis = -1    x = Convolution3D(nb_filter, nb0, nb1, nb2, subsample=subsample, border_mode=border_mode, bias=bias)(x)    if batch_norm:        assert not bias        x = BatchNormalization(axis=channel_axis)(x)    else:        assert bias    x = Activation('relu')(x)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]def conv_block(x, nb_filter, nb0, nb1, nb2, border_mode='same', subsample=(1, 1, 1), bias=True, batch_norm=False):    from keras.layers import Input, Dense, Convolution3D, MaxPooling3D, UpSampling3D, Reshape, Flatten, Activation    from keras.layers.normalization import BatchNormalization    from keras import backend as K    if K.image_dim_ordering() == "th":        channel_axis = 1    else:        channel_axis = -1    x = Convolution3D(nb_filter, nb0, nb1, nb2, subsample=subsample, border_mode=border_mode, bias=bias)(x)    if batch_norm:        assert not bias        x = BatchNormalization(axis=channel_axis)(x)    else:        assert bias    x = Activation('relu')(x)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]def inception3D(image_size, num_labels):    num_channels=1    inputs = Input(shape = (image_size, image_size, image_size, num_channels))    m = Convolution3D(32, 5, 5, 5, subsample=(1, 1, 1), activation='relu', border_mode='valid', input_shape=())(inputs)    m = MaxPooling3D(pool_size=(2, 2, 2), strides=None, border_mode='same')(m)    # inception module 0    branch1x1 = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)    branch3x3_reduce = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)    branch3x3 = Convolution3D(64, 3, 3, 3, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch3x3_reduce)    branch5x5_reduce = Convolution3D(16, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)    branch5x5 = Convolution3D(32, 5, 5, 5, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch5x5_reduce)    branch_pool = MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), border_mode='same')(m)    branch_pool_proj = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch_pool)    #m = merge([branch1x1, branch3x3, branch5x5, branch_pool_proj], mode='concat', concat_axis=-1)    from keras.layers import concatenate    m = concatenate([branch1x1, branch3x3, branch5x5, branch_pool_proj],axis=-1)    m = AveragePooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), border_mode='valid')(m)    m = Flatten()(m)    m = Dropout(0.7)(m)    # expliciately seperate Dense and Activation layers in order for projecting to structural feature space    m = Dense(num_labels, activation='linear')(m)    m = Activation('softmax')(m)    mod = KM.Model(input=inputs, output=m)    return mod 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]def build():        model = Sequential()        # Conv layer 1        model.add(Convolution3D(            input_shape = (14,32,32,32),            filters=64,            kernel_size=5,            padding='valid',     # Padding method            data_format='channels_first',        ))        model.add(LeakyReLU(alpha = 0.1))        # Dropout 1        model.add(Dropout(0.2))        # Conv layer 2        model.add(Convolution3D(            filters=64,            kernel_size=3,            padding='valid',     # Padding method            data_format='channels_first',        ))        model.add(LeakyReLU(alpha = 0.1))        # Maxpooling 1        model.add(MaxPooling3D(            pool_size=(2,2,2),            strides=None,            padding='valid',    # Padding method            data_format='channels_first'        ))        # Dropout 2        model.add(Dropout(0.4))        # FC 1        model.add(Flatten())        model.add(Dense(128)) # TODO changed to 64 for the CAM        model.add(LeakyReLU(alpha = 0.1))        # Dropout 3        model.add(Dropout(0.4))        # Fully connected layer 2 to shape (2) for 2 classes        model.add(Dense(2))        model.add(Activation('softmax'))        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]def srcnn(input_shape=(33,33,110,1)):    #for ROSIS  sensor    model = Sequential()    model.add(Convolution3D(64, 9, 9, 7, input_shape=input_shape, activation='relu'))    model.add(Convolution3D(32, 1, 1, 1, activation='relu'))    model.add(Convolution3D(9, 1, 1, 1, activation='relu'))    model.add(Convolution3D(1, 5, 5, 3))    model.compile(Adam(lr=0.00005), 'mse')    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]def auto_classifier_model(img_shape, encoding_dim=128, NUM_CHANNELS=1, num_of_class=2):    input_shape = (None, img_shape[0], img_shape[1], img_shape[2], NUM_CHANNELS)    mask_shape = (None, num_of_class)    # use relu activation for hidden layer to guarantee non-negative outputs are passed to the max pooling layer. In such case, as long as the output layer is linear activation, the network can still accomodate negative image intendities, just matter of shift back using the bias term    input_img = Input(shape=input_shape[1:])    mask = Input(shape=mask_shape[1:])    x = input_img    x = conv_block(x, 32, 3, 3, 3)    x = MaxPooling3D((2, 2, 2), padding ='same')(x)    x = conv_block(x, 32, 3, 3, 3)    x = MaxPooling3D((2, 2, 2), padding ='same')(x)    encoder_conv_shape = [_.value for _ in  x.get_shape()]          # x.get_shape() returns a list of tensorflow.python.framework.tensor_shape.Dimension objects    x = Flatten()(x)    encoded = Dense(encoding_dim, activation='relu', activity_regularizer=regularizers.l1(10e-5))(x)    encoder = Model(inputs=input_img, outputs=encoded)    x = BatchNormalization()(x)    x = Dense(encoding_dim, activation='relu', activity_regularizer=regularizers.l1(10e-5))(x)    x = Dense(128, activation = 'relu')(x)    x = Dense(num_of_class, activation = 'softmax')(x)        prob = x    # classifier output    classifier = Model(inputs=input_img, outputs=prob)    input_img_decoder = Input(shape=encoder.output_shape[1:])    x = input_img_decoder    x = Dense(np.prod(encoder_conv_shape[1:]), activation='relu')(x)    x = Reshape(encoder_conv_shape[1:])(x)    x = UpSampling3D((2, 2, 2))(x)    x = conv_block(x, 32, 3, 3, 3)    x = UpSampling3D((2, 2, 2))(x)    x = conv_block(x, 32, 3, 3, 3)    x = Convolution3D(1, (3, 3, 3), activation='linear', padding ='same')(x)    decoded = x    # autoencoder output    decoder = Model(inputs=input_img_decoder, outputs=decoded)        autoencoder = Sequential()    for l in encoder.layers:            autoencoder.add(l)    last = None    for l in decoder.layers:        last = l            autoencoder.add(l)    decoded = autoencoder(input_img)    auto_classifier = Model(inputs=input_img, outputs=[decoded, prob])    auto_classifier.summary()    return auto_classifier 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Convolution3D [as 别名]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)