This is my code below:
''' 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[:, 1:481] #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() 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]) ss = pd.Series(y_train) #indexing series needed for later Pandas Dummies one-hot vectors y_train_onehot = pd.get_dummies(ss) x_test=np.array([ i[1::] for i in new_test_data]) gg = pd.Series(y_test) y_test_onehot = pd.get_dummies(gg) # General Parameters learning_rate = 0.001 training_iters = 100000 batch_size = 33 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 with tf.variable_scope('cell_def'): lstm_cell = tf.nn.rnn_cell.LSTMCell(n_hidden, forget_bias=1.0) # Get lstm cell output with tf.variable_scope('rnn_def'): outputs, states = tf.nn.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 < training_iters: batch_x = np.split(x_train, 15) batch_y = np.split(y_train_onehot, 15) for index in range(len(batch_x)): ouh1 = batch_x[index] ouh2 = batch_y[index] # Reshape data to get 28 seq of 28 elements ouh1 = np.reshape(ouh1,(batch_size, n_steps, n_input)) sess.run(optimizer, feed_dict={x: ouh1, y: ouh2}) # Run optimization op (backprop) if step % display_step == 0: # Calculate batch accuracy acc = sess.run(accuracy, feed_dict={x: ouh1, y: ouh2}) # Calculate batch loss loss = sess.run(cost, feed_dict={x: ouh1, y: ouh2}) print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc)) step += 1 print("Optimization Finished!") and I am getting the below error that it seems i am re-iterating over the same variable on lines 92 and 97, and i am concerned that it might be a case of incompatibility with Tensorflow 0.10.0 on the RNN def side:
ValueError: Variable RNN/BasicLSTMCell/Linear/Matrix already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at: File "/home/mohsen/lstm_mnist.py", line 92, in RNN outputs, states = tf.nn.rnn(lstm_cell, x, dtype=tf.float32) File "/home/mohsen/lstm_mnist.py", line 97, in <module> pred = RNN(x, weights, biases) File "/home/mohsen/anaconda2/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 81, in execfile builtins.execfile(filename, *where) What could be the origin of this error and how i can resolve it?
EDIT: from the original repo where i build upon my code the same variable_scope problem persists https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py