title: Tensorflow: Numpy equivalent of tf batches and reshape
body: <p>I am trying to classify some images using Tensorflow using the LSTM method in image classification with one-hot encoding output and a softmax classifier at the last LSTM output. My dataset is CSV and had to research a lot in Numpy and Tensorflow on how to do some modifications. I'm still getting an error:</p>
  
  <pre><code>AttributeError: 'numpy.ndarray' object has no attribute 'next_batch'
  </code></pre>
  
  <p>which if you will see, i can't use <code>next_batch(batch_size)</code> along with my dataset and also the next <code>tf.reshape</code> needs to be replaced with its Numpy equivalent.</p>
  
  <p>My question: How should I correct these 2 issues?</p>
  
  <pre><code>'''
  Tensorflow LSTM classification of 16x30 images.
  '''
  
  from __future__ import print_function
  
  import tensorflow as tf
  from tensorflow.python.ops import rnn, rnn_cell
  import numpy as np
  from numpy import genfromtxt
  from sklearn.cross_validation import train_test_split
  import pandas as pd
  
  '''
  a Tensorflow LSTM that will sequentially input several lines from each single image 
  i.e. The Tensorflow graph will take a flat (1,480) features image as it was done in Multi-layer
  perceptron MNIST Tensorflow tutorial, but then reshape it in a sequential manner with 16 features each and 30 time_steps.
  '''
  
  blaine = genfromtxt('./Desktop/Blaine_CSV_lstm.csv',delimiter=',')  # CSV transform to array
  target = [row[0] for row in blaine]             # 1st column in CSV as the targets
  data = blaine[:, 1:480]                          #flat feature vectors
  X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.05, random_state=42)
  
  f=open('cs-training.csv','w')       #1st split for training
  for i,j in enumerate(X_train):
          k=np.append(np.array(y_train[i]),j   )
          f.write(",".join([str(s) for s in k]) + '\n')
  f.close()
  f=open('cs-testing.csv','w')        #2nd split for test
  for i,j in enumerate(X_test):
          k=np.append(np.array(y_test[i]),j   )
          f.write(",".join([str(s) for s in k]) + '\n')
  f.close()
  
  ss = pd.Series(y_train)     #indexing series needed for later Pandas Dummies one-hot vectors
  gg = pd.Series(y_test)
  
  new_data = genfromtxt('cs-training.csv',delimiter=',')  # Training data
  new_test_data = genfromtxt('cs-testing.csv',delimiter=',')  # Test data
  
  x_train=np.array([ i[1::] for i in new_data])
  y_train_onehot = pd.get_dummies(ss)
  
  x_test=np.array([ i[1::] for i in new_test_data])
  y_test_onehot = pd.get_dummies(gg)
  
  
  # General Parameters
  learning_rate = 0.001
  training_iters = 100000
  batch_size = 128
  display_step = 10
  
  # Tensorflow LSTM Network Parameters
  n_input = 16 # MNIST data input (img shape: 28*28)
  n_steps = 30 # timesteps
  n_hidden = 128 # hidden layer num of features
  n_classes = 20 # MNIST total classes (0-9 digits)
  
  # tf Graph input
  x = tf.placeholder("float", [None, n_steps, n_input])
  y = tf.placeholder("float", [None, n_classes])
  
  # Define weights
  weights = {
      'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
  }
  biases = {
      'out': tf.Variable(tf.random_normal([n_classes]))
  }
  
  
  def RNN(x, weights, biases):
  
      # Prepare data shape to match `rnn` function requirements
      # Current data input shape: (batch_size, n_steps, n_input)
      # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
  
      # Permuting batch_size and n_steps
      x = tf.transpose(x, [1, 0, 2])
      # Reshaping to (n_steps*batch_size, n_input)
      x = tf.reshape(x, [-1, n_input])
      # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
      x = tf.split(0, n_steps, x)
  
      # Define a lstm cell with tensorflow
      lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
  
      # Get lstm cell output
      outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)
  
      # Linear activation, using rnn inner loop last output
      return tf.matmul(outputs[-1], weights['out']) + biases['out']
  
  pred = RNN(x, weights, biases)
  
  # Define loss and optimizer
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
  
  # Evaluate model
  correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
  accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
  
  # Initializing the variables
  init = tf.initialize_all_variables()
  
  # Launch the graph
  with tf.Session() as sess:
      sess.run(init)
      step = 1
      # Keep training until reach max iterations
      while step * batch_size &lt; training_iters:
          x_train, y_train = new_data.next_batch(batch_size)
          # Reshape data to get 30 seq of 16 elements
          x_train = x_train.reshape((batch_size, n_steps, n_input))
          # Run optimization op (backprop)
          sess.run(optimizer, feed_dict={x: x_train, y: y_train})
          if step % display_step == 0:
              # Calculate batch accuracy
              acc = sess.run(accuracy, feed_dict={x: x_train, y: y_train})
              # Calculate batch loss
              loss = sess.run(cost, feed_dict={x: x_train, y: y_train})
              print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                    "{:.6f}".format(loss) + ", Training Accuracy= " + \
                    "{:.5f}".format(acc))
          step += 1
      print("Optimization Finished!")
  </code></pre>