title: AttributeError: 'dict' object has no attribute 'train' error when trying to implement a convolution neural network program using tensorflow in python
body: <p>Even though I am super new to the topic, I am trying to implement a CNN program that can be used to recognize images without using Keras. I am currently using python with Jupyter/Google Colab.</p>
  <p>After fixing a few other errors that came up in my code, I now have this error:</p>
  <pre><code>/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py:1761: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).
    warnings.warn('An interactive session is already active. This can '
  
  Training the model....
  ---------------------------------------------------------------------------
  AttributeError                            Traceback (most recent call last)
  &lt;ipython-input-28-1af250ed46b3&gt; in &lt;module&gt;()
      108     for i in range(num_iterations):
      109         # Get the next batch of images
  --&gt; 110         batch = mnist.train.next_batch(batch_size)              #third got an error for this line -
      111         # x_batch, y_batch = mnist.train.next_batch(batch_size)
      112 
  
  AttributeError: 'dict' object has no attribute 'train'
  </code></pre>
  <p>This is my current code:</p>
  <pre><code>!pip install tensorflow_datasets
  !pip install --upgrade tensorflow
  !pip install tensorflow-datasets
  !pip install mnist
  #!pip install tensorflow.examples.tutorials.mnist
  
  import argparse
  print ('argparse version: ', argparse.__version__)
  import mnist
  print ('MNIST version: ', mnist.__version__)
  import tensorflow_datasets
  print ('tensorflow_datasets version: ', tensorflow_datasets.__version__)
  import tensorflow.compat.v1 as tf
  print ('tf version: ', tf.__version__)
  tf.disable_v2_behavior()
  #from tensorflow.examples.tutorials.mnist import input_data
  
  
  #def build_arg_parser():
  #    parser = argparse.ArgumentParser(description='Build a CNN classifier \
  #            using MNIST data')
  #    parser.add_argument('--input-dir', dest='input_dir', type=str,
  #            default='./mnist_data', help='Directory for storing data')
  #    return parser
  
  def get_weights(shape):
      data = tf.truncated_normal(shape, stddev=0.1)
      return tf.Variable(data)
  
  def get_biases(shape):
      data = tf.constant(0.1, shape=shape)
      return tf.Variable(data)
  
  def create_layer(shape):
      # Get the weights and biases
      W = get_weights(shape)
      b = get_biases([shape[-1]])
  
      return W, b
  
  def convolution_2d(x, W):
      return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1],
              padding='SAME')
  
  def max_pooling(x):
      return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
              strides=[1, 2, 2, 1], padding='SAME')
  
  if __name__ == '__main__':
      #args = build_arg_parser().parse_args()
  
      # Get the MNIST data
      mnist = tensorflow_datasets.load('mnist')
  
      # The images are 28x28, so create the input layer
      # with 784 neurons (28x28=784)
      x = tf.placeholder(tf.float32, [None, 784])
  
      # Reshape 'x' into a 4D tensor
      x_image = tf.reshape(x, [-1, 28, 28, 1])
  
      # Define the first convolutional layer
      W_conv1, b_conv1 = create_layer([5, 5, 1, 32])
  
      # Convolve the image with weight tensor, add the
      # bias, and then apply the ReLU function
      h_conv1 = tf.nn.relu(convolution_2d(x_image, W_conv1) + b_conv1)
  
      # Apply the max pooling operator
      h_pool1 = max_pooling(h_conv1)
  
      # Define the second convolutional layer
      W_conv2, b_conv2 = create_layer([5, 5, 32, 64])
  
      # Convolve the output of previous layer with the
      # weight tensor, add the bias, and then apply
      # the ReLU function
      h_conv2 = tf.nn.relu(convolution_2d(h_pool1, W_conv2) + b_conv2)
  
      # Apply the max pooling operator
      h_pool2 = max_pooling(h_conv2)
  
      # Define the fully connected layer
      W_fc1, b_fc1 = create_layer([7 * 7 * 64, 1024])
  
      # Reshape the output of the previous layer
      h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
  
      # Multiply the output of previous layer by the
      # weight tensor, add the bias, and then apply
      # the ReLU function
      h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
      # Define the dropout layer using a probability placeholder
      # for all the neurons
      keep_prob = tf.placeholder(tf.float32)
      h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
      # Define the readout layer (output layer)
      W_fc2, b_fc2 = create_layer([1024, 10])
      y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  
      # Define the entropy loss and the optimizer
      y_loss = tf.placeholder(tf.float32, [None, 10])
      loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_loss, logits=y_conv))
      optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)
  
      # Define the accuracy computation
      predicted = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_loss, 1))
      accuracy = tf.reduce_mean(tf.cast(predicted, tf.float32))
  
      # Create and run a session
      sess = tf.InteractiveSession()
      init = tf.initialize_all_variables()
      sess.run(init)
  
      # Start training
      num_iterations = 21000
      batch_size = 75
      print('\nTraining the model....')
      for i in range(num_iterations):
          # Get the next batch of images
          batch = mnist.train.next_batch(batch_size)
  
          # Print progress
          if i % 50 == 0:
              cur_accuracy = accuracy.eval(feed_dict = {
                      x: batch[0], y_loss: batch[1], keep_prob: 1.0})
              print('Iteration', i, ', Accuracy =', cur_accuracy)
  
          Train on the current batch
          optimizer.run(feed_dict = {x: batch[0], y_loss: batch[1], keep_prob: 0.5})
  
      # Compute accuracy using test data
      print('Test accuracy =', accuracy.eval(feed_dict = {
              x: mnist.test.images, y_loss: mnist.test.labels,
              keep_prob: 1.0}))
  </code></pre>
  <p>I have found a few posts that have the exact same code as I do, but all of their implementations somehow run. I tried looking up solutions, but did not end up finding one that worked for me.</p>
  <p>One post I found said that &quot;base dictionaries don't have a 'train' attribute.&quot;
  This made me curious as to why I then got this error if dictionaries normally don't have these attributes, but others had this same code work.</p>
  <p>Another post used the line:</p>
  <pre><code>x_batch, y_batch = mnist.train.next_batch(batch_size)
  </code></pre>
  <p>instead of the line:</p>
  <pre><code>batch = mnist.train.next_batch(batch_size)
  </code></pre>
  <p>but neither seemed to work for me. None of the other changes/solutions I tried looking into ended up working either.</p>
  <p>Does anyone have any idea on how to fix this no attribute error?</p>