add restore code

Restore.py

+import tensorflow as tf
+import numpy as np
+import sys
+import os
+from functions import getSL
+
+def checkTest(test_data, test_sl, batchsz, bins): # , classes_test):
+	alphas_values = None
+	tot_pred = None
+	iterations = test_data.shape[0] / batchsz
+	if test_data.shape[0] % batchsz != 0:
+	    iterations+=1
+
+	for ibatch in range(iterations):
+		batch_limit, batch_x = getBatch(test_sl, test_data, ibatch, batchsz)
+		batch_mask = np.zeros((batch_limit.shape[0],bins))
+		for idx, val in enumerate(batch_limit):
+			batch_mask[idx,0:val] = 1.0
+
+		pred_temp = sess.run(testPrediction,feed_dict={
+											x_data:batch_x,
+											dropOut:0.,
+											is_training_ph:False,
+											seq_length:batch_limit,
+											mask:batch_mask
+											})
+
+		if tot_pred is None:
+			tot_pred = pred_temp
+		else:
+			tot_pred = np.concatenate((tot_pred, pred_temp),axis=0)
+
+		del batch_limit
+		del batch_x
+		del batch_mask
+
+	return tot_pred
+
+
+
+def getBatch(X, Y, i, batch_size):
+    start_id = i*batch_size
+    end_id = min( (i+1) * batch_size, X.shape[0])
+    batch_x = X[start_id:end_id]
+    batch_y = Y[start_id:end_id]
+    return batch_x, batch_y
+
+
+testDataFile = sys.argv[1]
+ckpt_path = sys.argv[2]
+outputFileName = sys.argv[3]
+
+test_data = np.load(testDataFile)
+test_sl = getSL(test_data)
+
+bins = test_data.shape[1]
+tf.reset_default_graph()
+
+results_path = "results/"
+
+if not os.path.exists(results_path):
+	os.makedirs(results_path)
+
+
+with tf.Session() as sess:
+	# Restore variables from disk.
+	model_saver = tf.train.import_meta_graph(ckpt_path+".meta")
+	model_saver.restore(sess, ckpt_path)
+
+	graph = tf.get_default_graph()
+
+	x_data = graph.get_tensor_by_name("x_data:0")
+	mask = graph.get_tensor_by_name("mask:0")
+	seq_length = graph.get_tensor_by_name("limits:0")
+	y = graph.get_tensor_by_name("y:0")
+
+	is_training_ph = graph.get_tensor_by_name("is_training:0")
+	dropOut = graph.get_tensor_by_name("drop_rate:0")
+	alphas_b = graph.get_tensor_by_name("alphas:0")
+	testPrediction = graph.get_tensor_by_name("pred_env/prediction:0")
+
+	print "Model restored. "+ckpt_path
+
+	test_prediction = checkTest(test_data, test_sl, 1024, bins)
+
+	np.save(results_path+outputFileName, test_prediction)