download mnist and resize to 32x32

-import tensorflow as tf
-
-# configuration for tensorflow 0.11 and 0.12 version
-try:
-    # tensorflow 0.12 version
-    image_summary = tf.summary.image
-    scalar_summary = tf.summary.scalar
-    histogram_summary = tf.summary.histogram
-    merge_summary = tf.summary.merge_all
-    SummaryWriter = tf.summary.FileWriter
-except:
-    # tensorflow <= 0.11 version
-    image_summary = tf.image_summary
-    scalar_summary = tf.scalar_summary
-    histogram_summary = tf.histogram_summary
-    merge_summary = tf.merge_all_summaries
-    SummaryWriter = tf.train.SummaryWriter
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-import tensorflow as tf
-    
-
-class batch_norm(object):
-    """Computes batch normalization operation
-    
-    Args:
-        x: input tensor of shape (batch_size, width, height, channels_in) or (batch_size, dim_in)
-        train: True or False;  At train mode, it normalizes the input with mini-batch statistics
-                               At test mode, it normalizes the input with the moving averages and variances
- 
-    Returns:
-        out: batch normalized output of the same shape with x
-    """
-    def __init__(self, name):
-        self.name = name
-    
-    def __call__(self, x, train=True):
-        out = tf.contrib.layers.batch_norm(x, decay=0.99, center=True, scale=True, activation_fn=None, 
-                                           updates_collections=None, is_training=train, scope=self.name)
-        return out
-    
-    
-def conv2d(x, channel_out, k_w=5, k_h=5, s_w=2, s_h=2, name=None):
-    """Computes convolution operation
-    
-    Args:
-        x: input tensor of shape (batch_size, width_in, heigth_in, channel_in)
-        channel_out: number of channel for output tensor
-        k_w: kernel width size; default is 5
-        k_h: kernel height size; default is 5
-        s_w: stride size for width; default is 2
-        s_h: stride size for heigth; default is 2
-        
-    Returns:
-        out: output tensor of shape (batch_size, width_out, height_out, channel_out)
-    """
-    channel_in = x.get_shape()[-1]
-    
-    with tf.variable_scope(name):
-        w = tf.get_variable('w', shape=[k_w, k_h, channel_in, channel_out], 
-                            initializer=tf.contrib.layers.xavier_initializer())
-        b = tf.get_variable('b', shape=[channel_out], initializer=tf.constant_initializer(0.0))
-        
-        out = tf.nn.conv2d(x, w, strides=[1, s_w, s_h, 1], padding='SAME') + b
-        
-        return out
-    
-    
-def deconv2d(x, output_shape, k_w=5, k_h=5, s_w=2, s_h=2, name=None):
-    """Computes deconvolution operation
-    
-    Args:
-        x: input tensor of shape (batch_size, width_in, height_in, channel_in)
-        output_shape: list corresponding to [batch_size, width_out, height_out, channel_out]
-        k_w: kernel width size; default is 5
-        k_h: kernel height size; default is 5
-        s_w: stride size for width; default is 2
-        s_h: stride size for heigth; default is 2
-        
-    Returns:
-        out: output tensor of shape (batch_size, width_out, hegith_out, channel_out)
-    """
-    channel_in = x.get_shape()[-1]
-    channel_out = output_shape[-1]
-    
-    
-    with tf.variable_scope(name):
-        w = tf.get_variable('w', shape=[k_w, k_h, channel_out, channel_in], 
-                            initializer=tf.contrib.layers.xavier_initializer())
-        b = tf.get_variable('b', shape=[channel_out], initializer=tf.constant_initializer(0.0))
-        
-        out = tf.nn.conv2d_transpose(x, filter=w, output_shape=output_shape, strides=[1, s_w, s_h, 1]) + b
-    
-        return out
-    
-def linear(x, dim_out, name=None):
-    """Computes linear transform (fully-connected layer)
-    
-    Args:
-        x: input tensor of shape (batch_size, dim_in)
-        dim_out: dimension for output tensor
-        
-    Returns:
-        out: output tensor of shape (batch_size, dim_out)
-    """
-    dim_in = x.get_shape()[-1]
-    
-    with tf.variable_scope(name):
-        w = tf.get_variable('w', shape=[dim_in, dim_out], initializer=tf.contrib.layers.xavier_initializer())
-        b = tf.get_variable('b', shape=[dim_out], initializer=tf.constant_initializer(0.0))
-        
-        out = tf.matmul(x, w) + b
-        
-        return out
-    
-
-def relu(x):
-    return tf.nn.relu(x)
-
-
-def lrelu(x, leak=0.2):
-    return tf.maximum(x, leak*x)
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+import numpy as np
+import pickle
+from PIL import Image
+from tensorflow.examples.tutorials.mnist import input_data
+
+
+def resize_images(image_arrays, size=[32, 32]):
+    # convert float type to integer 
+    image_arrays = (image_arrays * 255).astype('uint8')
+    
+    resized_image_arrays = np.zeros([image_arrays.shape[0]]+size)
+    for i, image_array in enumerate(image_arrays):
+        image = Image.fromarray(image_array)
+        resized_image = image.resize(size=size, resample=Image.ANTIALIAS)
+        
+        resized_image_arrays[i] = np.asarray(resized_image)
+    
+    return np.expand_dims(resized_image_arrays, 3)  
+
+def save_pickle(data, path):
+    with open(path, 'wb') as f:
+        pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+        print ('Saved %s..' %path)
+
+def main():
+    mnist = input_data.read_data_sets(train_dir='mnist')
+
+    train = {'X': resize_images(mnist.train.images.reshape(-1, 28, 28)),
+             'y': mnist.train.labels}
+    
+    test = {'X': resize_images(mnist.test.images.reshape(-1, 28, 28)),
+            'y': mnist.test.labels}
+        
+    save_pickle(train, 'mnist/train.pkl')
+    save_pickle(test, 'mnist/test.pkl')
+    
+    
+if __name__ == "__main__":
+    main()
+    
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