IndexError: list index out of range in model.fit()

问题

I am new in using tensorflow. I am trying to train my network with images of shape (16*16). I have divided 3 grayscale images of 512*512 into 16*16 and appended all. so i have 3072*16*16. while training I am getting error. I am using jupyter notebook.Can anyone please help me?

Here is the code

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)

and it is giving error:

_________________________________________________________________
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

回答1:

After my research, it became clearer that this is an already know issue and the official solution has not been released yet. Although, still there are some suggestions claimed to work.

It is suggested to update to the update nightly build version (pip install tf-nightly or pip install tf-nightly-gpu)

https://github.com/tensorflow/tensorflow/issues/21894#issuecomment-418552609