model files added

model.py

@@ -0,0 +1,84 @@
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+# from matplotlib import image as mpimg
+# from matplotlib import pyplot as plt
+
+class api():
+
+    height=64
+    width=64
+    channels=3
+    model_name = 'cnn_model'
+    classes = { 0 : 'Zero' , 1 : 'One' , 2 : 'Two' , 3 : 'Three' , 4 : 'Four' , 5 : 'Five' }
+
+    def reset_graph(self,seed=42):
+        tf.reset_default_graph()
+        tf.set_random_seed(seed)
+        np.random.seed(seed)
+
+
+    def __init__(self,upload_path='uploads'):
+
+        self.upload_path = upload_path
+
+        # self.model_name = 'cnn_model'
+        print('print',os.path.join('signs_api','{}.meta'.format(self.model_name)))
+
+        # self.import_meta = tf.train.import_meta_graph(os.path.join('signs_api','{}.meta'.format(self.model_name)))
+
+    def predict(self,im):
+
+        try :
+
+            # im = Image.open( os.path.join(self.upload_path,filename) )
+
+            #image size
+            size=(self.height,self.width)
+            #resize image
+            out = im.resize(size)
+
+            test_image =  np.array(out.getdata())
+
+            test_image = test_image.reshape((-1,self.height,self.width,self.channels))
+
+            # to make this notebook's output stable across runs
+            self.reset_graph()
+
+            # import meta from directory
+            # import_meta = tf.train.import_meta_graph('{}.meta'.format(self.model_name))
+            import_meta = tf.train.import_meta_graph(os.path.join('signs_api','{}.meta'.format(self.model_name)))
+
+            with tf.Session() as sess:
+
+                # tf.train.latest_checkpoint(<dir>) also works
+
+                import_meta.restore(sess,'{}.ckpt'.format( os.path.join('signs_api',self.model_name) ) )
+
+                # W1_val = sess.graph.get_tensor_by_name('W1:0')
+
+                # X_val = sess.graph.get_tensor_by_name('Placeholder:0')
+
+                ArgMax = sess.graph.get_tensor_by_name('ArgMax:0')
+
+                ArgMax_val = ArgMax.eval({ 'Placeholder:0' : test_image })
+
+                # graph = tf.get_default_graph()
+
+                # for op in graph.get_operations():
+                #     print(op.name)
+
+                # print('W1_val',W1_val)
+                # print('X_val',X_val)
+                print('ArgMax',ArgMax_val)
+                index = ArgMax_val.tolist()[0]
+                class_val = self.classes[index]
+
+                # os.remove(os.path.join(self.upload_path,filename))
+
+                return { 'value' : index , 'class' : class_val  }
+
+        except (OSError,IOError) as e:
+            print('error',e)
+            return { 'error' : True }

requriements.txt

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+tensorflow==1.15.0
+numpy
+Pillow