我有一个100行1035列的数据集，第一列为标签（0和1），剩下的列为用电负荷，请用pyts将1034个用电负荷生成格拉姆角场图片。请帮我写出将格拉姆角场图片输入到基于keras搭建的RESNET34网络实现标签二分类的代码

好的，针对您的问题，我可以提供如下的Python代码实现：

import numpy as np
from pyts.image import GramianAngularField
from keras.models import Model
from keras.layers import Input, Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import Adam

# 加载数据集
data = np.loadtxt('data.csv', delimiter=',')
X = data[:, 1:].reshape(-1, 1034)
y = data[:, 0]

# 生成格拉姆角场图片
gaf = GramianAngularField(image_size=1034, method='summation')
X_gaf = gaf.fit_transform(X)

# 构建RESNET34网络
inputs = Input(shape=(1034, 1034, 1))
x = Conv2D(64, kernel_size=(7, 7), strides=(2, 2), padding='same', activation='relu')(inputs)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

def residual_block(inputs, filters, strides):
    x = Conv2D(filters, kernel_size=(3, 3), strides=strides, padding='same', activation='relu')(inputs)
    x = Conv2D(filters, kernel_size=(3, 3), padding='same', activation='relu')(x)
    x = Conv2D(filters, kernel_size=(3, 3), padding='same', activation='relu')(x)
    shortcut = Conv2D(filters, kernel_size=(1, 1), strides=strides, padding='same', activation='relu')(inputs)
    x = x + shortcut
    return x

x = residual_block(x, filters=64, strides=(1, 1))
x = residual_block(x, filters=64, strides=(1, 1))
x = residual_block(x, filters=128, strides=(2, 2))
x = residual_block(x, filters=128, strides=(1, 1))
x = residual_block(x, filters=256, strides=(2, 2))
x = residual_block(x, filters=256, strides=(1, 1))
x = residual_block(x, filters=512, strides=(2, 2))
x = residual_block(x, filters=512, strides=(1, 1))
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
outputs = Dense(1, activation='sigmoid')(x)

model = Model(inputs, outputs)
model.summary()

# 编译模型
model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(X_gaf.reshape(-1, 1034, 1034, 1), y, epochs=50, batch_size=32, validation_split=0.2)

以上代码中，我们首先使用pyts库的GramianAngularField方法将原始数据转换成1034x1034的格拉姆角场图片，然后搭建了一个基于keras的RESNET34网络，该网络可以接收1034x1034的图片作为输入，并输出二分类的标签。最后我们使用Adam优化器编译模型，并在数据集上进行50个epoch的训练。