title: How Can I Load My Own Images Instead Of Mnist Dataset Images?
body: <p>Hi I am very frustrated that all the ml examples I see only use the MNIST dataset and don't use custom images and I would like to load in my own image dataset of pokemon. Here is my code:</p>
  
  <pre class="lang-py prettyprint-override"><code># -*- coding: utf-8 -*-
  """autoencoder.ipynb
  
  Automatically generated by Colaboratory.
  
  Original file is located at
      https://colab.research.google.com/drive/1P5rdEhs3lzcNMK9SWsOXdNq9nl74E54D
  """
  
  from keras.layers import Input, Dense
  from keras.models import Model
  
  # this is the size of our encoded representations
  encoding_dim = 32  # 32 floats -&gt; compression of factor 24.5, assuming the input is 784 floats
  
  # this is our input placeholder
  input_img = Input(shape=(784,))
  # "encoded" is the encoded representation of the input
  encoded = Dense(encoding_dim, activation='relu')(input_img)
  # "decoded" is the lossy reconstruction of the input
  decoded = Dense(784, activation='sigmoid')(encoded)
  
  # this model maps an input to its reconstruction
  autoencoder = Model(input_img, decoded)
  
  # this model maps an input to its encoded representation
  encoder = Model(input_img, encoded)
  # create a placeholder for an encoded (32-dimensional) input
  encoded_input = Input(shape=(encoding_dim,))
  # retrieve the last layer of the autoencoder model
  decoder_layer = autoencoder.layers[-1]
  # create the decoder model
  decoder = Model(encoded_input, decoder_layer(encoded_input))
  
  autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
  from keras.datasets import mnist
  import numpy as np
  (x_train, _), (x_test, _) = mnist.load_data()
  
  x_train = x_train.astype('float32') / 255.
  x_test = x_test.astype('float32') / 255.
  x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
  x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
  print(x_train.shape)
  print(x_test.shape)
  
  autoencoder.fit(x_train, x_train,
                  epochs=10,
                  batch_size=256,
                  shuffle=True,
                  validation_data=(x_test, x_test))
  
  # encode and decode some digits
  # note that we take them from the *test* set
  encoded_imgs = encoder.predict(x_test)
  decoded_imgs = decoder.predict(encoded_imgs)
  
  # use Matplotlib (don't ask)
  import matplotlib.pyplot as plt
  
  n = 10  # how many digits we will display
  plt.figure(figsize=(20, 4))
  for i in range(n):
      # display original
      ax = plt.subplot(2, n, i + 1)
      plt.imshow(x_test[i].reshape(28, 28))
      plt.gray()
      ax.get_xaxis().set_visible(False)
      ax.get_yaxis().set_visible(False)
  
      # display reconstruction
      ax = plt.subplot(2, n, i + 1 + n)
      plt.imshow(decoded_imgs[i].reshape(28, 28))
      plt.gray()
      ax.get_xaxis().set_visible(False)
      ax.get_yaxis().set_visible(False)
  plt.show()
  </code></pre>
  
  <p>Here is the structure of my images</p>
  
  <pre><code>images
    |_____abomasnow
          |___image
    |_____abra
          |___image
    |_____absol
          |___image
    |_____accelgor
          |___image
    ...
    |_____zweilous
          |___image
    |_____zubat
          |___image
    |_____zorua
          |___image
  </code></pre>
  
  <p>I've tried using <a href="https://github.com/Arlen0615/Convert-own-data-to-MNIST-format" rel="nofollow noreferrer">convert_to_mnist_format</a> but I got:
  ValueError: could not broadcast input array from shape (120,120,3) into shape (120,120,4)</p>
  
  <p>So I would like some help to make <a href="https://drive.google.com/drive/folders/1Tb7Rwek4cdl8WmIuVz_i6KZDn_s51BIC?usp=sharing" rel="nofollow noreferrer">this</a> dataset able to be read by the Autoencoder above</p>