working bp generation

Author: chen0040

demo/cifar10_tensorflow_classifier.py

@@ -0,0 +1,36 @@
+import tensorflow as tf
+import numpy as np
+from keras.datasets import cifar10
+
+from keras_image_classifier.library.cifar10_classifier import Cifar10Classifier
+
+
+def main():
+
+
+    (Xtrain, Ytrain), (Xtest, Ytest) = cifar10.load_data()
+
+    Xtest = Xtest.astype('float32') / 255
+
+    with tf.gfile.FastGFile('./models/tf/cnn_cifar10.pb', 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        _ = tf.import_graph_def(graph_def, name='')
+
+    with tf.Session() as sess:
+        [print(n.name) for n in sess.graph.as_graph_def().node]
+        predict_op = sess.graph.get_tensor_by_name('output_node0:0')
+
+        for i in range(Xtest.shape[0]):
+            x = Xtest[i]
+            x = x.astype(np.float) / 255
+            x = np.expand_dims(x, axis=0)
+            y = Ytest[i]
+            predicted = sess.run(predict_op, feed_dict={"conv2d_1_input:0": x,
+                                                        'dropout_1/keras_learning_phase:0': 0})
+            predicted_y = np.argmax(predicted, axis=1)
+            print('actual: ', y, '\tpredicted: ', predicted_y)
+
+
+if __name__ == '__main__':
+    main()

demo/cifar10_tensorflow_export_pb.py

@@ -0,0 +1,11 @@
+from keras_image_classifier.library.cifar10_classifier import Cifar10Classifier
+
+
+def main():
+    classifier = Cifar10Classifier()
+    classifier.load_model('./models')
+    classifier.export_tensorflow_model(output_fld='./models/tf')
+
+if __name__ == '__main__':
+    main()
+

demo/cnn_cifar10_predict.py

@@ -26,6 +26,7 @@ def predict_cifar10(filename):
     output = cifar10_model.predict_classes(input)[0]
     return output
 
+
 (Xtrain, Ytrain), (Xtest, Ytest) = cifar10.load_data()
 
 Xtest = Xtest.astype('float32') / 255

demo/cnn_cifar10_train.py

@@ -30,7 +30,7 @@ def main():
     print('score: ', score[0])
     print('accurarcy: ', score[1])
 
-    classifier.export_tensorflow_model(output_fld='./models/tf')
+
 
 
 if __name__ == '__main__':

demo/models/tf/cnn_cifar10.pb

@@ -40,8 +40,9 @@ def load_model(self, model_dir_path):
         self.nb_classes = config['nb_classes']
 
         self.model = model_from_json(open(self.get_architecture_file_path(model_dir_path)).read())
-        self.model.load_weights(self.get_weight_file_path(model_dir_path))
         self.model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
+        self.model.load_weights(self.get_weight_file_path(model_dir_path))
+
 
     def predict_label(self, filename):
         img = Image.open(filename)

keras_image_classifier/library/cifar10_classifier.py

@@ -5,4 +5,4 @@ numpy
 h5py
 pillow
 scikit-learn
-tensorflow
+tensorflow == 1.6.0rc1