我已经修改了here中找到的代码。但是我在输入中遇到尺寸错误，如下所示：

  

ValueError：检查输入时出错：预期InputLayer具有4   尺寸，但具有形状的数组（无，无）

这是我修改的代码（我正在colab上运行）：

#Power data classification/regression with cnn
import numpy as np
import tensorflow as tf
from tensorflow import keras
import pandas as pd
import csv as csv
import keras.backend as K
from sklearn.preprocessing import MinmaxScaler # For normalizing data
print("TensorFlow version:",tf.__version__)

!wget https://raw.githubusercontent.com/sibyjackgrove/cnn-on-Wind-Power-Data/master/MISO_power_data_classification_labels.csv
!wget https://raw.githubusercontent.com/sibyjackgrove/cnn-on-Wind-Power-Data/master/MISO_power_data_input.csv

#Read total rows in csv file without loading into memory
def data_set_size(csv_file):
    with open(csv_file) as csvfile:
        csv_rows = 0
        for _ in csvfile:
            csv_rows += 1
    return csv_rows-1            #Remove header from count and return

csv_file = "./MISO_power_data_classification_labels.csv"
n_train = data_set_size(csv_file)
print("Training data set size:",n_train)

#Python generator to supply batches of traning data during training with loading full data set to memory
def power_data_generator(batch_size,gen_type=''):
    valid_size = max(1,np.int(0.2*batch_size))
    while 1:
        df_input=pd.read_csv('./MISO_power_data_input.csv',usecols =['Wind_MWh','actual_Load_MWh'],chunksize =24*(batch_size+valid_size),iterator=True)
        df_target=pd.read_csv('./MISO_power_data_classification_labels.csv',usecols =['Mean Wind Power','Standard Deviation','WindShare'],chunksize =batch_size+valid_size,iterator=True)
        for chunk,chunk2 in  zip(df_input,df_target):
            scaler = MinmaxScaler() # Define limits for normalize data
            InputX = chunk.values
            InputX = scaler.fit_transform(InputX) # Normalize input data
            InputY = chunk2.values
            InputY = scaler.fit_transform(InputY) # Normalize output data
            if gen_type =='training':
                yield (InputX[0:batch_size],InputY[0:batch_size])
            elif gen_type =='validation':
                yield (InputX[batch_size:batch_size+valid_size],InputY[batch_size:batch_size+valid_size])

#Define model using Keras
Yclasses = 3 #Number of output classes

def nossa_metrica(y_true,y_pred):
    diff = y_true - y_pred
    count = K.sum(K.cast(K.equal(diff,K.zeros_like(diff)),'int8')) # Count how many times y_true = y_pred
    return count/n_train

model = keras.Sequential([
    tf.keras.layers.Input(shape=(2,24,1),name='InputLayer'),tf.keras.layers.Conv2D(filters=4,kernel_size=(2,6),strides=(1,activation='relu',name='ConvLayer1'),kernel_size=(1,name='ConvLayer2'),tf.keras.layers.flatten(name="flatten"),tf.keras.layers.Dense(units = 8,name='FeedForward1'),tf.keras.layers.Dense(units = Yclasses,name='OutputLayer'),])

model.compile(loss='mse',optimizer='adam',verbose = 2,metrics = [nossa_metrica])
model.summary()

samples_per_batch = 5
train_generator= power_data_generator(batch_size=samples_per_batch,gen_type='training')
valid_generator= power_data_generator(batch_size=samples_per_batch,gen_type='validation')
number_of_batches = np.int32(n_train/(samples_per_batch+max(1,np.int32(0.2*samples_per_batch)))) 
#Training starts
history = model.fit(train_generator,steps_per_epoch= number_of_batches,epochs=200,validation_data=valid_generator,validation_steps=number_of_batches,verbose=2)

如果有人可以在这里说些什么，我将非常感激！