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- Step = 1799 | Tensorflow Accuracy = 1.0
- Step = 1799 | My Accuracy = 0.0363355780022
- Step = 1800 | Tensorflow Accuracy = 1.0
- Step = 1800 | My Accuracy = 0.0364694929089
- Traceback (most recent call last):
- File "CNN-LSTM-seg-reg-sigmoid.py", line 290, in <module>
- saver.save(sess, save_path)
- File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1085, in save
- self.export_meta_graph(meta_graph_filename)
- File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1103, in export_meta_graph
- add_shapes=True),
- File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2175, in as_graph_def
- result, _ = self._as_graph_def(from_version, add_shapes)
- File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2138, in _as_graph_def
- raise ValueError("GraphDef cannot be larger than 2GB.")
- ValueError: GraphDef cannot be larger than 2GB.
- # Make prediction
- im = Image.open('/home/volcart/Documents/Data/input_crops/temp data0001.tif')
- batch_x = np.array(im)
- batch_x = batch_x.reshape((1, n_input_x, n_input_y))
- batch_x = batch_x.astype(float)
- prediction = sess.run(pred, feed_dict={x: batch_x})
- prediction = tf.sigmoid(prediction.reshape((n_input_x * n_input_y, n_classes)))
- prediction = prediction.eval().reshape((n_input_x, n_input_y, n_classes))
- # Initializing the variables
- init = tf.initialize_all_variables()
- saver = tf.train.Saver()
- gpu_options = tf.GPUOptions()
- config = tf.ConfigProto(gpu_options=gpu_options)
- config.gpu_options.allow_growth = True
- # Launch the graph
- with tf.Session(config=config) as sess:
- sess.run(init)
- summary = tf.train.SummaryWriter('/tmp/logdir/', sess.graph) #initialize graph for tensorboard
- step = 1
- # Import data
- data = scroll_data.read_data('/home/volcart/Documents/Data/')
- # Keep training until reach max iterations
- while step * batch_size < training_iters:
- batch_x, batch_y = data.train.next_batch(batch_size)
- # Run optimization op (backprop)
- batch_x = batch_x.reshape((batch_size, n_input_x, n_input_y))
- batch_y = batch_y.reshape((batch_size, n_input_x, n_input_y))
- batch_y = convert_to_2_channel(batch_y, batch_size)
- sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
- step = step + 1
- loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
- y: batch_y})
- # Make prediction
- im = Image.open('/home/volcart/Documents/Data/input_crops/temp data0001.tif')
- batch_x = np.array(im)
- batch_x = batch_x.reshape((1, n_input_x, n_input_y))
- batch_x = batch_x.astype(float)
- prediction = sess.run(pred, feed_dict={x: batch_x})
- prediction = tf.sigmoid(prediction.reshape((n_input_x * n_input_y, n_classes)))
- prediction = prediction.eval().reshape((n_input_x, n_input_y, n_classes))
- # Temp arrays are to splice the prediction n_input_x x n_input_y x 2
- # into 2 matrices n_input_x x n_input_y
- temp_arr1 = np.empty((n_input_x, n_input_y))
- for i in xrange(n_input_x):
- for j in xrange(n_input_x):
- for k in xrange(n_classes):
- if k == 0:
- temp_arr1[i][j] = 1 - prediction[i][j][k]
- my_acc = accuracy_custom(temp_arr1,batch_y[0,:,:,0])
- print "Step = " + str(step) + " | Tensorflow Accuracy = " + str(acc)
- print "Step = " + str(step) + " | My Accuracy = " + str(my_acc)
- if step % 100 == 0:
- save_path = "/home/volcart/Documents/CNN-LSTM-reg-model/CNN-LSTM-seg-step-" + str(step) + "-model.ckpt"
- saver.save(sess, save_path)
- csv_file = "/home/volcart/Documents/CNN-LSTM-reg/CNNLSTMreg-step-" + str(step) + "-accuracy-" + str(my_acc) + ".csv"
- np.savetxt(csv_file, temp_arr1, delimiter=",")
- prediction = tf.sigmoid(prediction.reshape((n_input_x * n_input_y, n_classes)))
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