Python TensorflowUtils.weight_variable方法代码示例

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def inference_simple(dataset):
    with tf.name_scope("conv1") as scope:
        W1 = utils.weight_variable([5, 5, 1, 32], name="W1")
        b1 = utils.bias_variable([32], name="b1")
        tf.histogram_summary("W1", W1)
        tf.histogram_summary("b1", b1)
        h_conv1 = tf.nn.relu(utils.conv2d_basic(dataset, W1, b1), name="h_conv1")
        h_pool1 = utils.max_pool_2x2(h_conv1)

    with tf.name_scope("conv2") as scope:
        W2 = utils.weight_variable([3, 3, 32, 64], name="W2")
        b2 = utils.bias_variable([64], name="b2")
        tf.histogram_summary("W2", W2)
        tf.histogram_summary("b2", b2)
        h_conv2 = tf.nn.relu(utils.conv2d_basic(h_pool1, W2, b2), name="h_conv2")
        h_pool2 = utils.max_pool_2x2(h_conv2)

    with tf.name_scope("fc") as scope:
        image_size = IMAGE_SIZE // 4
        h_flat = tf.reshape(h_pool2, [-1, image_size * image_size * 64])
        W_fc = utils.weight_variable([image_size * image_size * 64, NUM_LABELS], name="W_fc")
        b_fc = utils.bias_variable([NUM_LABELS], name="b_fc")
        tf.histogram_summary("W_fc", W_fc)
        tf.histogram_summary("b_fc", b_fc)
        pred = tf.matmul(h_flat, W_fc) + b_fc

    return pred

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def inference(data):
    with tf.variable_scope("inference") as scope:
        W_1 = utils.weight_variable([IMAGE_SIZE * IMAGE_SIZE * 50], name="W_1")
        b_1 = utils.bias_variable([50], name="b_1")
        h_1 = tf.nn.relu(tf.matmul(data, tf.reshape(W_1, [IMAGE_SIZE * IMAGE_SIZE, 50])) + b_1, name='h_1')
        utils.add_activation_summary(h_1)

        W_2 = utils.weight_variable([50 * 50], name="W_2")
        b_2 = utils.bias_variable([50], name="b_2")
        h_2 = tf.nn.relu(tf.matmul(h_1, tf.reshape(W_2, [50, 50])) + b_2, name='h_2')
        utils.add_activation_summary(h_2)

        W_3 = utils.weight_variable([50 * 50], name="W_3")
        b_3 = utils.bias_variable([50], name="b_3")
        h_3 = tf.nn.relu(tf.matmul(h_2, tf.reshape(W_3, [50, 50])) + b_3, name='h_3')
        utils.add_activation_summary(h_3)

        W_4 = utils.weight_variable([50 * 50], name="W_4")
        b_4 = utils.bias_variable([50], name="b_4")
        h_4 = tf.nn.relu(tf.matmul(h_3, tf.reshape(W_4, [50, 50])) + b_4, name='h_4')
        utils.add_activation_summary(h_4)

        W_final = utils.weight_variable([50 * 10], name="W_final")
        b_final = utils.bias_variable([10], name="b_final")
        pred = tf.nn.softmax(tf.matmul(h_4, tf.reshape(W_final, [50, 10])) + b_final, name='h_final')
        # utils.add_activation_summary(pred)
    return pred

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def encoder_conv(image):
    with tf.name_scope("enc_conv1") as scope:
        W_conv1 = utils.weight_variable([3, 3, 3, 32], name="W_conv1")
        b_conv1 = utils.bias_variable([32], name="b_conv1")
        h_conv1 = tf.nn.tanh(utils.conv2d_strided(image, W_conv1, b_conv1))

    with tf.name_scope("enc_conv2") as scope:
        W_conv2 = utils.weight_variable([3, 3, 32, 64], name="W_conv2")
        b_conv2 = utils.bias_variable([64], name="b_conv2")
        h_conv2 = tf.nn.tanh(utils.conv2d_strided(h_conv1, W_conv2, b_conv2))

    with tf.name_scope("enc_conv3") as scope:
        W_conv3 = utils.weight_variable([3, 3, 64, 128], name="W_conv3")
        b_conv3 = utils.bias_variable([128], name="b_conv3")
        h_conv3 = tf.nn.tanh(utils.conv2d_strided(h_conv2, W_conv3, b_conv3))

    with tf.name_scope("enc_conv4") as scope:
        W_conv4 = utils.weight_variable([3, 3, 128, 256], name="W_conv4")
        b_conv4 = utils.bias_variable([256], name="b_conv4")
        h_conv4 = tf.nn.tanh(utils.conv2d_strided(h_conv3, W_conv4, b_conv4))

    with tf.name_scope("enc_fc") as scope:
        image_size = IMAGE_SIZE // 16
        h_conv4_flatten = tf.reshape(h_conv4, [-1, image_size * image_size * 256])
        W_fc5 = utils.weight_variable([image_size * image_size * 256, 512], name="W_fc5")
        b_fc5 = utils.bias_variable([512], name="b_fc5")
        encoder_val = tf.matmul(h_conv4_flatten, W_fc5) + b_fc5

    return encoder_val

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def inference_conv(image):
    # incomplete :/
    image_reshaped = tf.reshape(image, [-1, IMAGE_SIZE, IMAGE_SIZE, 1])
    with tf.name_scope("conv1") as scope:
        W_conv1 = utils.weight_variable([3, 3, 1, 32], name="W_conv1")
        b_conv1 = utils.bias_variable([32], name="b_conv1")
        add_to_reg_loss_and_summary(W_conv1, b_conv1)
        h_conv1 = tf.nn.tanh(utils.conv2d_basic(image_reshaped, W_conv1, b_conv1))

    with tf.name_scope("conv2") as scope:
        W_conv2 = utils.weight_variable([3, 3, 32, 64], name="W_conv2")
        b_conv2 = utils.bias_variable([64], name="b_conv2")
        add_to_reg_loss_and_summary(W_conv2, b_conv2)
        h_conv2 = tf.nn.tanh(utils.conv2d_strided(h_conv1, W_conv2, b_conv2))

    with tf.name_scope("conv3") as scope:
        W_conv3 = utils.weight_variable([3, 3, 64, 128], name="W_conv3")
        b_conv3 = utils.bias_variable([128], name="b_conv3")
        add_to_reg_loss_and_summary(W_conv3, b_conv3)
        h_conv3 = tf.nn.tanh(utils.conv2d_strided(h_conv2, W_conv3, b_conv3))

    with tf.name_scope("conv4") as scope:
        W_conv4 = utils.weight_variable([3, 3, 128, 256], name="W_conv4")
        b_conv4 = utils.bias_variable([256], name="b_conv4")
        add_to_reg_loss_and_summary(W_conv4, b_conv4)
        h_conv4 = tf.nn.tanh(utils.conv2d_strided(h_conv3, W_conv4, b_conv4))

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def generator(z, train_mode):
    with tf.variable_scope("generator") as scope:
        W_0 = utils.weight_variable([FLAGS.z_dim, 64 * GEN_DIMENSION / 2 * IMAGE_SIZE / 16 * IMAGE_SIZE / 16],
                                    name="W_0")
        b_0 = utils.bias_variable([64 * GEN_DIMENSION / 2 * IMAGE_SIZE / 16 * IMAGE_SIZE / 16], name="b_0")
        z_0 = tf.matmul(z, W_0) + b_0
        h_0 = tf.reshape(z_0, [-1, IMAGE_SIZE / 16, IMAGE_SIZE / 16, 64 * GEN_DIMENSION / 2])
        h_bn0 = utils.batch_norm(h_0, 64 * GEN_DIMENSION / 2, train_mode, scope="gen_bn0")
        h_relu0 = tf.nn.relu(h_bn0, name='relu0')
        utils.add_activation_summary(h_relu0)

        # W_1 = utils.weight_variable([5, 5, 64 * GEN_DIMENSION/2, 64 * GEN_DIMENSION], name="W_1")
        # b_1 = utils.bias_variable([64 * GEN_DIMENSION/2], name="b_1")
        # deconv_shape = tf.pack([tf.shape(h_relu0)[0], IMAGE_SIZE / 16, IMAGE_SIZE / 16, 64 * GEN_DIMENSION/2])
        # h_conv_t1 = utils.conv2d_transpose_strided(h_relu0, W_1, b_1, output_shape=deconv_shape)
        # h_bn1 = utils.batch_norm(h_conv_t1, 64 * GEN_DIMENSION/2, train_mode, scope="gen_bn1")
        # h_relu1 = tf.nn.relu(h_bn1, name='relu1')
        # utils.add_activation_summary(h_relu1)

        W_2 = utils.weight_variable([5, 5, 64 * GEN_DIMENSION / 4, 64 * GEN_DIMENSION / 2],
                                    name="W_2")
        b_2 = utils.bias_variable([64 * GEN_DIMENSION / 4], name="b_2")
        deconv_shape = tf.pack([tf.shape(h_relu0)[0], IMAGE_SIZE / 8, IMAGE_SIZE / 8, 64 * GEN_DIMENSION / 4])
        h_conv_t2 = utils.conv2d_transpose_strided(h_relu0, W_2, b_2, output_shape=deconv_shape)
        h_bn2 = utils.batch_norm(h_conv_t2, 64 * GEN_DIMENSION / 4, train_mode, scope="gen_bn2")
        h_relu2 = tf.nn.relu(h_bn2, name='relu2')
        utils.add_activation_summary(h_relu2)

        W_3 = utils.weight_variable([5, 5, 64 * GEN_DIMENSION / 8, 64 * GEN_DIMENSION / 4],
                                    name="W_3")
        b_3 = utils.bias_variable([64 * GEN_DIMENSION / 8], name="b_3")
        deconv_shape = tf.pack([tf.shape(h_relu2)[0], IMAGE_SIZE / 4, IMAGE_SIZE / 4, 64 * GEN_DIMENSION / 8])
        h_conv_t3 = utils.conv2d_transpose_strided(h_relu2, W_3, b_3, output_shape=deconv_shape)
        h_bn3 = utils.batch_norm(h_conv_t3, 64 * GEN_DIMENSION / 8, train_mode, scope="gen_bn3")
        h_relu3 = tf.nn.relu(h_bn3, name='relu3')
        utils.add_activation_summary(h_relu3)

        W_4 = utils.weight_variable([5, 5, 64 * GEN_DIMENSION / 16, 64 * GEN_DIMENSION / 8],
                                    name="W_4")
        b_4 = utils.bias_variable([64 * GEN_DIMENSION / 16], name="b_4")
        deconv_shape = tf.pack([tf.shape(h_relu3)[0], IMAGE_SIZE / 2, IMAGE_SIZE / 2, 64 * GEN_DIMENSION / 16])
        h_conv_t4 = utils.conv2d_transpose_strided(h_relu3, W_4, b_4, output_shape=deconv_shape)
        h_bn4 = utils.batch_norm(h_conv_t4, 64 * GEN_DIMENSION / 16, train_mode, scope="gen_bn4")
        h_relu4 = tf.nn.relu(h_bn4, name='relu4')
        utils.add_activation_summary(h_relu4)

        W_5 = utils.weight_variable([5, 5, NUM_OF_CHANNELS, 64 * GEN_DIMENSION / 16], name="W_5")
        b_5 = utils.bias_variable([NUM_OF_CHANNELS], name="b_5")
        deconv_shape = tf.pack([tf.shape(h_relu4)[0], IMAGE_SIZE, IMAGE_SIZE, NUM_OF_CHANNELS])
        h_conv_t5 = utils.conv2d_transpose_strided(h_relu4, W_5, b_5, output_shape=deconv_shape)
        pred_image = tf.nn.tanh(h_conv_t5, name='pred_image')
        utils.add_activation_summary(pred_image)

    return pred_image

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def inference_res(input_image):
    W1 = utils.weight_variable([3, 3, 3, 32])
    b1 = utils.bias_variable([32])
    hconv_1 = tf.nn.relu(utils.conv2d_basic(input_image, W1, b1))
    h_norm = utils.local_response_norm(hconv_1)
    bottleneck_1 = utils.bottleneck_unit(h_norm, 16, 16, down_stride=True, name="res_1")
    bottleneck_2 = utils.bottleneck_unit(bottleneck_1, 8, 8, down_stride=True, name="res_2")
    bottleneck_3 = utils.bottleneck_unit(bottleneck_2, 16, 16, up_stride=True, name="res_3")
    bottleneck_4 = utils.bottleneck_unit(bottleneck_3, 32, 32, up_stride=True, name="res_4")
    W5 = utils.weight_variable([3, 3, 32, 3])
    b5 = utils.bias_variable([3])
    out = tf.nn.tanh(utils.conv2d_basic(bottleneck_4, W5, b5))
    return out

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def decoder_fc(z):
    with tf.variable_scope("decoder") as scope:
        Wd_fc1 = utils.weight_variable([FLAGS.z_dim, 50], name="Wd_fc1")
        bd_fc1 = utils.bias_variable([50], name="bd_fc1")
        hd_relu1 = activation_function(tf.matmul(z, Wd_fc1) + bd_fc1, name="hdfc_1")

        Wd_fc2 = utils.weight_variable([50, 50], name="Wd_fc2")
        bd_fc2 = utils.bias_variable([50], name="bd_fc2")
        hd_relu2 = activation_function(tf.matmul(hd_relu1, Wd_fc2) + bd_fc2, name="hdfc_2")

        Wd_fc3 = utils.weight_variable([50, IMAGE_SIZE * IMAGE_SIZE], name="Wd_fc3")
        bd_fc3 = utils.bias_variable([IMAGE_SIZE * IMAGE_SIZE], name="bd_fc3")
        pred_image = tf.matmul(hd_relu2, Wd_fc3) + bd_fc3
    return pred_image

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def encoder_fc(images):
    with tf.variable_scope("encoder") as scope:
        W_fc1 = utils.weight_variable([IMAGE_SIZE * IMAGE_SIZE, 50], name="W_fc1")
        b_fc1 = utils.bias_variable([50], name="b_fc1")
        h_relu1 = activation_function(tf.matmul(images, W_fc1) + b_fc1, name="hfc_1")

        W_fc2 = utils.weight_variable([50, 50], name="W_fc2")
        b_fc2 = utils.bias_variable([50], name="b_fc2")
        h_relu2 = activation_function(tf.matmul(h_relu1, W_fc2) + b_fc2, name="hfc_2")

        W_fc3 = utils.weight_variable([50, FLAGS.z_dim], name="W_fc3")
        b_fc3 = utils.bias_variable([FLAGS.z_dim], name="b_fc3")
        mu = tf.add(tf.matmul(h_relu2, W_fc3), b_fc3, name="mu")
        utils.add_activation_summary(mu)

        W_fc4 = utils.weight_variable([50, FLAGS.z_dim], name="W_fc4")
        b_fc4 = utils.bias_variable([FLAGS.z_dim], name="b_fc4")
        log_var = tf.add(tf.matmul(h_relu2, W_fc4), b_fc4, name="log_var")
        utils.add_activation_summary(log_var)

    return mu, log_var

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def discriminator(input_images, train_mode):
    # dropout_prob = 1.0
    # if train_mode:
    #     dropout_prob = 0.5
    W_conv0 = utils.weight_variable([5, 5, NUM_OF_CHANNELS, 64 * 1], name="W_conv0")
    b_conv0 = utils.bias_variable([64 * 1], name="b_conv0")
    h_conv0 = utils.conv2d_strided(input_images, W_conv0, b_conv0)
    h_bn0 = h_conv0  # utils.batch_norm(h_conv0, 64 * 1, train_mode, scope="disc_bn0")
    h_relu0 = utils.leaky_relu(h_bn0, 0.2, name="h_relu0")
    utils.add_activation_summary(h_relu0)

    W_conv1 = utils.weight_variable([5, 5, 64 * 1, 64 * 2], name="W_conv1")
    b_conv1 = utils.bias_variable([64 * 2], name="b_conv1")
    h_conv1 = utils.conv2d_strided(h_relu0, W_conv1, b_conv1)
    h_bn1 = utils.batch_norm(h_conv1, 64 * 2, train_mode, scope="disc_bn1")
    h_relu1 = utils.leaky_relu(h_bn1, 0.2, name="h_relu1")
    utils.add_activation_summary(h_relu1)

    W_conv2 = utils.weight_variable([5, 5, 64 * 2, 64 * 4], name="W_conv2")
    b_conv2 = utils.bias_variable([64 * 4], name="b_conv2")
    h_conv2 = utils.conv2d_strided(h_relu1, W_conv2, b_conv2)
    h_bn2 = utils.batch_norm(h_conv2, 64 * 4, train_mode, scope="disc_bn2")
    h_relu2 = utils.leaky_relu(h_bn2, 0.2, name="h_relu2")
    utils.add_activation_summary(h_relu2)

    W_conv3 = utils.weight_variable([5, 5, 64 * 4, 64 * 8], name="W_conv3")
    b_conv3 = utils.bias_variable([64 * 8], name="b_conv3")
    h_conv3 = utils.conv2d_strided(h_relu2, W_conv3, b_conv3)
    h_bn3 = utils.batch_norm(h_conv3, 64 * 8, train_mode, scope="disc_bn3")
    h_relu3 = utils.leaky_relu(h_bn3, 0.2, name="h_relu3")
    utils.add_activation_summary(h_relu3)

    shape = h_relu3.get_shape().as_list()
    h_3 = tf.reshape(h_relu3, [FLAGS.batch_size, (IMAGE_SIZE // 16) * (IMAGE_SIZE // 16) * shape[3]])
    W_4 = utils.weight_variable([h_3.get_shape().as_list()[1], 1], name="W_4")
    b_4 = utils.bias_variable([1], name="b_4")
    h_4 = tf.matmul(h_3, W_4) + b_4

    return tf.nn.sigmoid(h_4), h_4, h_relu3

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def inference(data, keep_prob):
    with tf.variable_scope("inference") as scope:
        weight_variable_size = IMAGE_SIZE * IMAGE_SIZE * 50 + 50 * 50 * 3 + 50 * 10
        bias_variable_size = 4 * 50 + 10
        print (weight_variable_size + bias_variable_size)
        variable = utils.weight_variable([weight_variable_size + bias_variable_size], name="variables")
        weight_variable = tf.slice(variable, [0], [weight_variable_size], name="weights")
        bias_variable = tf.slice(variable, [weight_variable_size], [bias_variable_size], name="biases")
        weight_offset = 0
        bias_offset = 0
        W_1 = tf.slice(weight_variable, [weight_offset], [IMAGE_SIZE * IMAGE_SIZE * 50], name="W_1")
        b_1 = tf.slice(bias_variable, [bias_offset], [50], name="b_1")
        h_1_relu = tf.nn.relu(tf.matmul(data, tf.reshape(W_1, [IMAGE_SIZE * IMAGE_SIZE, 50])) + b_1, name='h_1')
        h_1 = tf.nn.dropout(h_1_relu, keep_prob)
        utils.add_activation_summary(h_1)

        weight_offset += IMAGE_SIZE * IMAGE_SIZE * 50
        bias_offset += 50

        W_2 = tf.slice(weight_variable, [weight_offset], [50 * 50], name="W_2")
        b_2 = tf.slice(bias_variable, [bias_offset], [50], name="b_2")
        h_2_relu = tf.nn.relu(tf.matmul(h_1, tf.reshape(W_2, [50, 50])) + b_2, name='h_2')
        h_2 = tf.nn.dropout(h_2_relu, keep_prob)
        utils.add_activation_summary(h_2)

        weight_offset += 50 * 50
        bias_offset += 50

        W_3 = tf.slice(weight_variable, [weight_offset], [50 * 50], name="W_3")
        b_3 = tf.slice(bias_variable, [bias_offset], [50], name="b_3")
        h_3_relu = tf.nn.relu(tf.matmul(h_2, tf.reshape(W_3, [50, 50])) + b_3, name='h_3')
        h_3 = tf.nn.dropout(h_3_relu, keep_prob)
        utils.add_activation_summary(h_3)

        weight_offset += 50 * 50
        bias_offset += 50

        W_4 = tf.slice(weight_variable, [weight_offset], [50 * 50], name="W_4")
        b_4 = tf.slice(bias_variable, [bias_offset], [50], name="b_4")
        h_4_relu = tf.nn.relu(tf.matmul(h_3, tf.reshape(W_4, [50, 50])) + b_4, name='h_4')
        h_4 = tf.nn.dropout(h_4_relu, keep_prob)
        utils.add_activation_summary(h_4)

        weight_offset += 50 * 50
        bias_offset += 50

        W_final = tf.slice(weight_variable, [weight_offset], [50 * 10], name="W_final")
        b_final = tf.slice(bias_variable, [bias_offset], [10], name="b_final")
        pred = tf.nn.softmax(tf.matmul(h_4, tf.reshape(W_final, [50, 10])) + b_final, name='h_final')
        # utils.add_activation_summary(pred)
    return pred

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def inferece(dataset, prob):
    with tf.name_scope("conv1") as scope:
        W_conv1 = utils.weight_variable([5, 5, 1, 32])
        b_conv1 = utils.bias_variable([32])
        tf.histogram_summary("W_conv1", W_conv1)
        tf.histogram_summary("b_conv1", b_conv1)
        h_conv1 = utils.conv2d_basic(dataset, W_conv1, b_conv1)
        h_1 = tf.nn.relu(h_conv1)
        h_pool1 = utils.max_pool_2x2(h_1)
        add_to_regularization_loss(W_conv1, b_conv1)

    with tf.name_scope("conv2") as scope:
        W_conv2 = utils.weight_variable([3, 3, 32, 64])
        b_conv2 = utils.bias_variable([64])
        tf.histogram_summary("W_conv2", W_conv2)
        tf.histogram_summary("b_conv2", b_conv2)
        h_conv2 = utils.conv2d_basic(h_pool1, W_conv2, b_conv2)
        h_2 = tf.nn.relu(h_conv2)
        h_pool2 = utils.max_pool_2x2(h_2)
        add_to_regularization_loss(W_conv2, b_conv2)

    with tf.name_scope("fc_1") as scope:
        image_size = IMAGE_SIZE / 4
        h_flat = tf.reshape(h_pool2, [-1, image_size * image_size * 64])
        W_fc1 = utils.weight_variable([image_size * image_size * 64, 256])
        b_fc1 = utils.bias_variable([256])
        tf.histogram_summary("W_fc1", W_fc1)
        tf.histogram_summary("b_fc1", b_fc1)
        h_fc1 = tf.nn.relu(tf.matmul(h_flat, W_fc1) + b_fc1)
        h_fc1_dropout = tf.nn.dropout(h_fc1, prob)
    with tf.name_scope("fc_2") as scope:
        W_fc2 = utils.weight_variable([256, NUM_LABELS])
        b_fc2 = utils.bias_variable([NUM_LABELS])
        tf.histogram_summary("W_fc2", W_fc2)
        tf.histogram_summary("b_fc2", b_fc2)
        pred = tf.matmul(h_fc1, W_fc2) + b_fc2

    return pred

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def inference(dataset):
    with tf.name_scope("conv1") as scope:
        W1 = utils.weight_variable([5, 5, 1, 32], name="W1")
        b1 = utils.bias_variable([32], name="b1")
        tf.histogram_summary("W1", W1)
        tf.histogram_summary("b1", b1)
        h_conv1 = utils.conv2d_basic(dataset, W1, b1)
        h_norm1 = utils.local_response_norm(h_conv1)
        h_1 = tf.nn.relu(h_norm1, name="conv1")
        h_pool1 = utils.max_pool_2x2(h_1)

    with tf.name_scope("conv2") as scope:
        W2 = utils.weight_variable([3, 3, 32, 64], name="W2")
        b2 = utils.bias_variable([64], name="b2")
        tf.histogram_summary("W2", W2)
        tf.histogram_summary("b2", b2)
        h_conv2 = utils.conv2d_basic(h_pool1, W2, b2)
        h_norm2 = utils.local_response_norm(h_conv2)
        h_2 = tf.nn.relu(h_norm2, name="conv2")
        h_pool2 = utils.max_pool_2x2(h_2)

    with tf.name_scope("conv3") as scope:
        W3 = utils.weight_variable([3, 3, 64, 128], name="W3")
        b3 = utils.bias_variable([128], name="b3")
        tf.histogram_summary("W3", W3)
        tf.histogram_summary("b3", b3)
        h_conv3 = utils.conv2d_basic(h_pool2, W3, b3)
        h_norm3 = utils.local_response_norm(h_conv3)
        h_3 = tf.nn.relu(h_norm3, name="conv3")
        h_pool3 = utils.max_pool_2x2(h_3)

    with tf.name_scope("conv4") as scope:
        W4 = utils.weight_variable([3, 3, 128, 256], name="W4")
        b4 = utils.bias_variable([256], name="b4")
        tf.histogram_summary("W4", W4)
        tf.histogram_summary("b4", b4)
        h_conv4 = utils.conv2d_basic(h_pool3, W4, b4)
        h_norm4 = utils.local_response_norm(h_conv4)
        h_4 = tf.nn.relu(h_norm4, name="conv4")

    with tf.name_scope("fc1") as scope:
        image_size = IMAGE_SIZE // 8
        h_flat = tf.reshape(h_4, [-1, image_size * image_size * 256])
        W_fc1 = utils.weight_variable([image_size * image_size * 256, 512], name="W_fc1")
        b_fc1 = utils.bias_variable([512], name="b_fc1")
        tf.histogram_summary("W_fc1", W_fc1)
        tf.histogram_summary("b_fc1", b_fc1)
        h_fc1 = tf.nn.relu(tf.matmul(h_flat, W_fc1) + b_fc1)

    with tf.name_scope("fc2") as scope:
        W_fc2 = utils.weight_variable([512, NUM_LABELS], name="W_fc2")
        b_fc2 = utils.bias_variable([NUM_LABELS], name="b_fc2")
        tf.histogram_summary("W_fc2", W_fc2)
        tf.histogram_summary("b_fc2", b_fc2)
        pred = tf.matmul(h_fc1, W_fc2) + b_fc2

    return pred

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def decoder_conv(embedding):
    image_size = IMAGE_SIZE // 16
    with tf.name_scope("dec_fc") as scope:
        W_fc1 = utils.weight_variable([512, image_size * image_size * 256], name="W_fc1")
        b_fc1 = utils.bias_variable([image_size * image_size * 256], name="b_fc1")
        h_fc1 = tf.nn.relu(tf.matmul(embedding, W_fc1) + b_fc1)

    with tf.name_scope("dec_conv1") as scope:
        h_reshaped = tf.reshape(h_fc1, tf.pack([tf.shape(h_fc1)[0], image_size, image_size, 256]))
        W_conv_t1 = utils.weight_variable([3, 3, 128, 256], name="W_conv_t1")
        b_conv_t1 = utils.bias_variable([128], name="b_conv_t1")
        deconv_shape = tf.pack([tf.shape(h_fc1)[0], 2 * image_size, 2 * image_size, 128])
        h_conv_t1 = tf.nn.relu(
            utils.conv2d_transpose_strided(h_reshaped, W_conv_t1, b_conv_t1, output_shape=deconv_shape))

    with tf.name_scope("dec_conv2") as scope:
        W_conv_t2 = utils.weight_variable([3, 3, 64, 128], name="W_conv_t2")
        b_conv_t2 = utils.bias_variable([64], name="b_conv_t2")
        deconv_shape = tf.pack([tf.shape(h_conv_t1)[0], 4 * image_size, 4 * image_size, 64])
        h_conv_t2 = tf.nn.relu(
            utils.conv2d_transpose_strided(h_conv_t1, W_conv_t2, b_conv_t2, output_shape=deconv_shape))

    with tf.name_scope("dec_conv3") as scope:
        W_conv_t3 = utils.weight_variable([3, 3, 32, 64], name="W_conv_t3")
        b_conv_t3 = utils.bias_variable([32], name="b_conv_t3")
        deconv_shape = tf.pack([tf.shape(h_conv_t2)[0], 8 * image_size, 8 * image_size, 32])
        h_conv_t3 = tf.nn.relu(
            utils.conv2d_transpose_strided(h_conv_t2, W_conv_t3, b_conv_t3, output_shape=deconv_shape))

    with tf.name_scope("dec_conv4") as scope:
        W_conv_t4 = utils.weight_variable([3, 3, 3, 32], name="W_conv_t4")
        b_conv_t4 = utils.bias_variable([3], name="b_conv_t4")
        deconv_shape = tf.pack([tf.shape(h_conv_t3)[0], IMAGE_SIZE, IMAGE_SIZE, 3])
        pred_image = utils.conv2d_transpose_strided(h_conv_t3, W_conv_t4, b_conv_t4, output_shape=deconv_shape)

    return pred_image

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def main(argv=None):
    utils.maybe_download_and_extract(FLAGS.model_dir, DATA_URL)
    model_data = get_model_data()
    invert_image = get_image(FLAGS.image_path)
    print invert_image.shape

    mean = model_data['normalization'][0][0][0]
    mean_pixel = np.mean(mean, axis=(0, 1))

    processed_image = utils.process_image(invert_image, mean_pixel).astype(np.float32)
    weights = np.squeeze(model_data['layers'])

    invert_net = vgg_net(weights, processed_image)

    dummy_image = utils.weight_variable(invert_image.shape, stddev=np.std(invert_image) * 0.1)
    tf.histogram_summary("Image Output", dummy_image)
    image_net = vgg_net(weights, dummy_image)

    with tf.Session() as sess:
        invert_layer_features = invert_net[INVERT_LAYER].eval()
        loss = 2 * tf.nn.l2_loss(image_net[INVERT_LAYER] - invert_layer_features) / invert_layer_features.size
        tf.scalar_summary("Loss", loss)

        summary_op = tf.merge_all_summaries()
        train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)

        best_loss = float('inf')
        best = None
        summary_writer = tf.train.SummaryWriter(FLAGS.log_dir)
        sess.run(tf.initialize_all_variables())

        for i in range(1, MAX_ITERATIONS):
            train_op.run()

            if i % 10 == 0 or i == MAX_ITERATIONS - 1:
                this_loss = loss.eval()
                print('Step %d' % (i)),
                print('    total loss: %g' % this_loss)
                summary_writer.add_summary(summary_op.eval(), global_step=i)
                if this_loss < best_loss:
                    best_loss = this_loss
                    best = dummy_image.eval()
                    output = utils.unprocess_image(best.reshape(invert_image.shape[1:]), mean_pixel)
                    scipy.misc.imsave("invert_check.png", output)

    output = utils.unprocess_image(best.reshape(invert_image.shape[1:]), mean_pixel)
    scipy.misc.imsave("output.png", output)

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import weight_variable [as 别名]
def encoder(dataset, train_mode):
    with tf.variable_scope("Encoder"):
        with tf.name_scope("enc_conv1") as scope:
            W_conv1 = utils.weight_variable_xavier_initialized([3, 3, 3, 32], name="W_conv1")
            b_conv1 = utils.bias_variable([32], name="b_conv1")
            h_conv1 = utils.conv2d_strided(dataset, W_conv1, b_conv1)
            h_bn1 = utils.batch_norm(h_conv1, 32, train_mode, scope="conv1_bn")
            h_relu1 = tf.nn.relu(h_bn1)

        with tf.name_scope("enc_conv2") as scope:
            W_conv2 = utils.weight_variable_xavier_initialized([3, 3, 32, 64], name="W_conv2")
            b_conv2 = utils.bias_variable([64], name="b_conv2")
            h_conv2 = utils.conv2d_strided(h_relu1, W_conv2, b_conv2)
            h_bn2 = utils.batch_norm(h_conv2, 64, train_mode, scope="conv2_bn")
            h_relu2 = tf.nn.relu(h_bn2)

        with tf.name_scope("enc_conv3") as scope:
            W_conv3 = utils.weight_variable_xavier_initialized([3, 3, 64, 128], name="W_conv3")
            b_conv3 = utils.bias_variable([128], name="b_conv3")
            h_conv3 = utils.conv2d_strided(h_relu2, W_conv3, b_conv3)
            h_bn3 = utils.batch_norm(h_conv3, 128, train_mode, scope="conv3_bn")
            h_relu3 = tf.nn.relu(h_bn3)

        with tf.name_scope("enc_conv4") as scope:
            W_conv4 = utils.weight_variable_xavier_initialized([3, 3, 128, 256], name="W_conv4")
            b_conv4 = utils.bias_variable([256], name="b_conv4")
            h_conv4 = utils.conv2d_strided(h_relu3, W_conv4, b_conv4)
            h_bn4 = utils.batch_norm(h_conv4, 256, train_mode, scope="conv4_bn")
            h_relu4 = tf.nn.relu(h_bn4)

        with tf.name_scope("enc_conv5") as scope:
            W_conv5 = utils.weight_variable_xavier_initialized([3, 3, 256, 512], name="W_conv5")
            b_conv5 = utils.bias_variable([512], name="b_conv5")
            h_conv5 = utils.conv2d_strided(h_relu4, W_conv5, b_conv5)
            h_bn5 = utils.batch_norm(h_conv5, 512, train_mode, scope="conv5_bn")
            h_relu5 = tf.nn.relu(h_bn5)

        with tf.name_scope("enc_fc") as scope:
            image_size = IMAGE_SIZE // 32
            h_relu5_flatten = tf.reshape(h_relu5, [-1, image_size * image_size * 512])
            W_fc = utils.weight_variable([image_size * image_size * 512, 1024], name="W_fc")
            b_fc = utils.bias_variable([1024], name="b_fc")
            encoder_val = tf.matmul(h_relu5_flatten, W_fc) + b_fc

    return encoder_val