Python normalization.batch_normalization方法代码示例

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import batch_normalization [as 别名]
def block35(net, scale=1.0, activation="relu"):
    tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1')))
    tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1')))
    tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3')))
    tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
    tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3')))
    tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3')))
    tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3)
    tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
    net += scale * tower_out
    if activation:
        if isinstance(activation, str):
            net = activations.get(activation)(net)
        elif hasattr(activation, '__call__'):
            net = activation(net)
        else:
            raise ValueError("Invalid Activation.")
    return net 

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import batch_normalization [as 别名]
def block17(net, scale=1.0, activation="relu"):
    tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
    tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
    tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7')))
    tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1')))
    tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3)
    tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
    net += scale * tower_out
    if activation:
        if isinstance(activation, str):
            net = activations.get(activation)(net)
        elif hasattr(activation, '__call__'):
            net = activation(net)
        else:
            raise ValueError("Invalid Activation.")
    return net 

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import batch_normalization [as 别名]
def block8(net, scale=1.0, activation="relu"):
    tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1')))
    tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1')))
    tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3')))
    tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1')))
    tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3)
    tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1')))
    net += scale * tower_out
    if activation:
        if isinstance(activation, str):
            net = activations.get(activation)(net)
        elif hasattr(activation, '__call__'):
            net = activation(net)
        else:
            raise ValueError("Invalid Activation.")
    return net 

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import batch_normalization [as 别名]
def conv_bn_relu(net, nf, fs, scope,
                 padding='same',
                 strides=1,
                 reuse=False,
                 weights_init='variance_scaling',
                 weight_decay=0.,
                 activation='relu'):

    if padding == 'wrap':
        padding = 'valid'
        curr = wrap_pad_rows(net, (fs-1)//2)
    else:
        curr = net

    netout = conv_2d(curr, nf, fs,
                     activation='linear',
                     padding=padding,
                     scope=scope,
                     reuse=reuse,
                     strides=[1, strides, strides, 1],
                     weights_init=weights_init,
                     regularizer='L2',
                     weight_decay=weight_decay)

    netout = batch_normalization(netout, scope=scope, reuse=reuse)
    netout = getattr(tflearn.activations, activation)(netout)

    return netout 

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import batch_normalization [as 别名]
def construct_inceptionv4onfire(x,y, training=True, enable_batch_norm=True):

    network = input_data(shape=[None, y, x, 3])

    #stem of inceptionV4

    conv1_3_3 = conv_2d(network,32,3,strides=2,activation='relu',name='conv1_3_3_s2',padding='valid')
    conv2_3_3 = conv_2d(conv1_3_3,32,3,activation='relu',name='conv2_3_3')
    conv3_3_3 = conv_2d(conv2_3_3,64,3,activation='relu',name='conv3_3_3')
    b_conv_1_pool = max_pool_2d(conv3_3_3,kernel_size=3,strides=2,padding='valid',name='b_conv_1_pool')
    if enable_batch_norm:
        b_conv_1_pool = batch_normalization(b_conv_1_pool)
    b_conv_1_conv = conv_2d(conv3_3_3,96,3,strides=2,padding='valid',activation='relu',name='b_conv_1_conv')
    b_conv_1 = merge([b_conv_1_conv,b_conv_1_pool],mode='concat',axis=3)

    b_conv4_1_1 = conv_2d(b_conv_1,64,1,activation='relu',name='conv4_3_3')
    b_conv4_3_3 = conv_2d(b_conv4_1_1,96,3,padding='valid',activation='relu',name='conv5_3_3')

    b_conv4_1_1_reduce = conv_2d(b_conv_1,64,1,activation='relu',name='b_conv4_1_1_reduce')
    b_conv4_1_7 = conv_2d(b_conv4_1_1_reduce,64,[1,7],activation='relu',name='b_conv4_1_7')
    b_conv4_7_1 = conv_2d(b_conv4_1_7,64,[7,1],activation='relu',name='b_conv4_7_1')
    b_conv4_3_3_v = conv_2d(b_conv4_7_1,96,3,padding='valid',name='b_conv4_3_3_v')
    b_conv_4 = merge([b_conv4_3_3_v, b_conv4_3_3],mode='concat',axis=3)

    b_conv5_3_3 = conv_2d(b_conv_4,192,3,padding='valid',activation='relu',name='b_conv5_3_3',strides=2)
    b_pool5_3_3 = max_pool_2d(b_conv_4,kernel_size=3,padding='valid',strides=2,name='b_pool5_3_3')
    if enable_batch_norm:
        b_pool5_3_3 = batch_normalization(b_pool5_3_3)
    b_conv_5 = merge([b_conv5_3_3,b_pool5_3_3],mode='concat',axis=3)
    net = b_conv_5

    # inceptionV4 modules

    net=inception_block_a(net)

    net=inception_block_b(net)

    net=inception_block_c(net)

    pool5_7_7=global_avg_pool(net)
    if(training):
        pool5_7_7=dropout(pool5_7_7,0.4)
    loss = fully_connected(pool5_7_7, 2,activation='softmax')

    if(training):
        network = regression(loss, optimizer='rmsprop',
                             loss='categorical_crossentropy',
                             learning_rate=0.001)
    else:
        network=loss

    model = tflearn.DNN(network, checkpoint_path='inceptionv4onfire',
                        max_checkpoints=1, tensorboard_verbose=0)

    return model

################################################################################ 

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import batch_normalization [as 别名]
def build_modelB(optimizer=HYPERPARAMS.optimizer, optimizer_param=HYPERPARAMS.optimizer_param, 
    learning_rate=HYPERPARAMS.learning_rate, keep_prob=HYPERPARAMS.keep_prob,
    learning_rate_decay=HYPERPARAMS.learning_rate_decay, decay_step=HYPERPARAMS.decay_step):

    images_network = input_data(shape=[None, NETWORK.input_size, NETWORK.input_size, 1], name='input1')
    images_network = conv_2d(images_network, 64, 3, activation=NETWORK.activation)
    #images_network = local_response_normalization(images_network)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = conv_2d(images_network, 128, 3, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = conv_2d(images_network, 256, 3, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = dropout(images_network, keep_prob=keep_prob)
    images_network = fully_connected(images_network, 4096, activation=NETWORK.activation)
    images_network = dropout(images_network, keep_prob=keep_prob)
    images_network = fully_connected(images_network, 1024, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_fully_connected_layers:
        images_network = batch_normalization(images_network)

    if NETWORK.use_landmarks or NETWORK.use_hog_and_landmarks:
        if NETWORK.use_hog_sliding_window_and_landmarks:
            landmarks_network = input_data(shape=[None, 2728], name='input2')
        elif NETWORK.use_hog_and_landmarks:
            landmarks_network = input_data(shape=[None, 208], name='input2')
        else:
            landmarks_network = input_data(shape=[None, 68, 2], name='input2')
        landmarks_network = fully_connected(landmarks_network, 1024, activation=NETWORK.activation)
        if NETWORK.use_batchnorm_after_fully_connected_layers:
            landmarks_network = batch_normalization(landmarks_network)
        landmarks_network = fully_connected(landmarks_network, 128, activation=NETWORK.activation)
        if NETWORK.use_batchnorm_after_fully_connected_layers:
            landmarks_network = batch_normalization(landmarks_network)
        images_network = fully_connected(images_network, 128, activation=NETWORK.activation)
        network = merge([images_network, landmarks_network], 'concat', axis=1)
    else:
        network = images_network
    network = fully_connected(network, NETWORK.output_size, activation='softmax')

    if optimizer == 'momentum':
        optimizer = Momentum(learning_rate=learning_rate, momentum=optimizer_param, 
                    lr_decay=learning_rate_decay, decay_step=decay_step)
    elif optimizer == 'adam':
        optimizer = Adam(learning_rate=learning_rate, beta1=optimizer_param, beta2=learning_rate_decay)
    else:
        print( "Unknown optimizer: {}".format(optimizer))
    network = regression(network, optimizer=optimizer, loss=NETWORK.loss, learning_rate=learning_rate, name='output')

    return network 

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import batch_normalization [as 别名]
def build_modelA(optimizer=HYPERPARAMS.optimizer, optimizer_param=HYPERPARAMS.optimizer_param, 
    learning_rate=HYPERPARAMS.learning_rate, keep_prob=HYPERPARAMS.keep_prob,
    learning_rate_decay=HYPERPARAMS.learning_rate_decay, decay_step=HYPERPARAMS.decay_step):

    images_network = input_data(shape=[None, NETWORK.input_size, NETWORK.input_size, 1], name='input1')
    images_network = conv_2d(images_network, 64, 5, activation=NETWORK.activation)
    #images_network = local_response_normalization(images_network)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = conv_2d(images_network, 64, 5, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = max_pool_2d(images_network, 3, strides = 2)
    images_network = conv_2d(images_network, 128, 4, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_conv_layers:
        images_network = batch_normalization(images_network)
    images_network = dropout(images_network, keep_prob=keep_prob)
    images_network = fully_connected(images_network, 1024, activation=NETWORK.activation)
    if NETWORK.use_batchnorm_after_fully_connected_layers:
        images_network = batch_normalization(images_network)

    if NETWORK.use_landmarks or NETWORK.use_hog_and_landmarks:
        if NETWORK.use_hog_sliding_window_and_landmarks:
            landmarks_network = input_data(shape=[None, 2728], name='input2')
        elif NETWORK.use_hog_and_landmarks:
            landmarks_network = input_data(shape=[None, 208], name='input2')
        else:
            landmarks_network = input_data(shape=[None, 68, 2], name='input2')
        landmarks_network = fully_connected(landmarks_network, 1024, activation=NETWORK.activation)
        if NETWORK.use_batchnorm_after_fully_connected_layers:
            landmarks_network = batch_normalization(landmarks_network)
        landmarks_network = fully_connected(landmarks_network, 40, activation=NETWORK.activation)
        if NETWORK.use_batchnorm_after_fully_connected_layers:
            landmarks_network = batch_normalization(landmarks_network)
        images_network = fully_connected(images_network, 40, activation=NETWORK.activation)
        network = merge([images_network, landmarks_network], 'concat', axis=1)
    else:
        network = images_network
    network = fully_connected(network, NETWORK.output_size, activation='softmax')

    if optimizer == 'momentum':
        optimizer = Momentum(learning_rate=learning_rate, momentum=optimizer_param, 
                    lr_decay=learning_rate_decay, decay_step=decay_step)
    elif optimizer == 'adam':
        optimizer = Adam(learning_rate=learning_rate, beta1=optimizer_param, beta2=learning_rate_decay)
    else:
        print( "Unknown optimizer: {}".format(optimizer))
    network = regression(network, optimizer=optimizer, loss=NETWORK.loss, learning_rate=learning_rate, name='output')

    return network