title: IndexError: list index out of range in model.fit()
body: <p>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? </p>
  
  <p>Here is the code</p>
  
  <pre><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)
  </code></pre>
  
  <p>and it is giving error:</p>
  
  <pre><code>_________________________________________________________________
  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)
  &lt;ipython-input-20-49da746cee1b&gt; in &lt;module&gt;()
       24 model.summary()
       25 # Store training stats
  ---&gt; 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,
  -&gt; 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)):
  --&gt; 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
  </code></pre>