train_model.py is the training script in tensorflow

build_model.py

@@ -1,5 +1,5 @@
 #building the model in tensorflow
-#from data_clean.py import *
+from data_clean import *
 import tensorflow as tf
 
 def model_inputs():

train_model.py

@@ -0,0 +1,54 @@
+# training the built model_inputs
+
+from build_model import *
+# Set the Hyperparameters
+epochs = 100
+batch_size = 64
+rnn_size = 256
+num_layers = 2
+learning_rate = 0.005
+keep_probability = 0.75
+
+# Build the graph
+train_graph = tf.Graph()
+# Set the graph to default to ensure that it is ready for training
+with train_graph.as_default():
+
+    # Load the model inputs
+    input_data, targets, lr, keep_prob, summary_length, max_summary_length, text_length = model_inputs()
+
+    # Create the training and inference logits
+    training_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),
+                                                      targets,
+                                                      keep_prob,
+                                                      text_length,
+                                                      summary_length,
+                                                      max_summary_length,
+                                                      len(vocab_to_int)+1,
+                                                      rnn_size,
+                                                      num_layers,
+                                                      vocab_to_int,
+                                                      batch_size)
+
+    # Create tensors for the training logits and inference logits
+    training_logits = tf.identity(training_logits.rnn_output, 'logits')
+    inference_logits = tf.identity(inference_logits.sample_id, name='predictions')
+
+    # Create the weights for sequence_loss
+    masks = tf.sequence_mask(summary_length, max_summary_length, dtype=tf.float32, name='masks')
+
+    with tf.name_scope("optimization"):
+        # Loss function
+        cost = tf.contrib.seq2seq.sequence_loss(
+            training_logits,
+            targets,
+            masks)
+
+        # Optimizer
+        optimizer = tf.train.AdamOptimizer(learning_rate)
+
+        # Gradient Clipping
+        gradients = optimizer.compute_gradients(cost)
+        capped_gradients = [(tf.clip_by_value(grad, -5., 5.), var) for grad, var in gradients if grad is not None]
+        train_op = optimizer.apply_gradients(capped_gradients)
+print("Graph is built.")