我是张量流的新手。我正在尝试使用形状（16*16）的图像来训练我的网络。我将3张512*512的灰度图像分成16*16并全部附加。所以我有3072*16*16。训练时我遇到错误。我正在使用 jupyter 笔记本。有人可以帮助我吗？
这是代码
import tensorflow as tf
import numpy as np
from numpy import newaxis
import glob
import os
from PIL import Image,ImageOps
import random
from os.path import join
import matplotlib.pyplot as plt
from tensorflow import keras
TRAIN_PATH = 'dataset/2/*.jpg'
LOGS_Path = "dataset/logs/"
CHECKPOINTS_PATH = 'dataset/checkpoints/'
BETA = .75
EXP_NAME = f"beta_{BETA}"
files_list = glob.glob(join(TRAIN_PATH))
leng=len(files_list)
new_cover = []
for i in range(leng):
img_cover_path = files_list[i]
for j in range (0,512,16):
for k in range (0,512,16):
img_cover = Image.open(img_cover_path)
area=(k,j,k+16,j+16)
img_cover1=img_cover.crop(area)
img_cover1 = np.array(ImageOps.fit(img_cover1(16,16)),dtype=np.float32)
img_cover1 /= 255.
n1.append(img_cover1)
new_cover.append(n1)
new_cover = np.array(new_cover)
new_cover1=np.swapaxes(new_cover, 1,3)
tf.reset_default_graph()
model=keras.Sequential()
#1st
model.add(keras.layers.Conv2D(64, (3, 3), strides=1,padding='SAME', input_shape = (16, 16, 3072))) #number of filters,shape of filter,input image size,activation function
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation(activation='relu'))
#2
model.add(keras.layers.Conv2D(64, (3, 3),strides=1,padding='SAME')) #number of filters,shape of filter,input image size,activation function
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation(activation='relu'))
#3
model.add(keras.layers.Conv2D(64, (3, 3),strides=1,padding='SAME')) #number of filters,shape of filter,input image size,activation function
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation(activation='relu'))
#4
model.add(keras.layers.Conv2D(64, (3, 3),strides=1,padding='SAME')) #number of filters,shape of filter,input image size,activation function
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation(activation='relu'))
#message
#compiling
model.compile(optimizer = tf.train.AdamOptimizer(0.001),loss='mse', metrics = ['accuracy'])
model.summary()
# Store training stats
model.fit(x=new_cover1,y=None, batch_size=32, epochs=1, verbose=1, callbacks=None, validation_split=0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)
它给出了错误：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 16, 16, 64) 1769536
_________________________________________________________________
batch_normalization (BatchNo (None, 16, 16, 64) 256
_________________________________________________________________
activation (Activation) (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 16, 16, 64) 36928
_________________________________________________________________
batch_normalization_1 (Batch (None, 16, 16, 64) 256
_________________________________________________________________
activation_1 (Activation) (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 16, 16, 64) 36928
_________________________________________________________________
batch_normalization_2 (Batch (None, 16, 16, 64) 256
_________________________________________________________________
activation_2 (Activation) (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 16, 16, 64) 36928
_________________________________________________________________
batch_normalization_3 (Batch (None, 16, 16, 64) 256
_________________________________________________________________
activation_3 (Activation) (None, 16, 16, 64) 0
=================================================================
Total params: 1,881,344
Trainable params: 1,880,832
Non-trainable params: 512
_________________________________________________________________
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-20-49da746cee1b> in <module>()
24 model.summary()
25 # Store training stats
---> 26 model.fit(x=new_cover1,y=None, batch_size=32, epochs=1, verbose=1, callbacks=None, validation_split=0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)
27
28 #return model
~\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)
1654 initial_epoch=initial_epoch,
1655 steps_per_epoch=steps_per_epoch,
-> 1656 validation_steps=validation_steps)
1657
1658 def evaluate(self,
~\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py in fit_loop(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)
135 indices_for_conversion_to_dense = []
136 for i in range(len(feed)):
--> 137 if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
138 indices_for_conversion_to_dense.append(i)
139
IndexError: list index out of range
