
import argparse
import pickle
import tensorflow.keras as keras
import numpy as np
import sys
import cv2

parser = argparse.ArgumentParser(description='Train the network given ')

parser.add_argument('-b', '--database-path', dest='imgdb_path',
                    help='Path to the image database containing test data.'
                         'Default is img.db in current folder.')
parser.add_argument('-m', '--model-path', dest='model_path',
                    help='Store the trained model using this path. Default is model.h5.')

args = parser.parse_args()

imgdb_path = "img.db"
model_path = "model.h5"
res = {"x": 16, "y": 16}

if args.model_path is not None:
    model_path = args.model_path

if args.imgdb_path is not None:
    imgdb_path = args.imgdb_path

with open(imgdb_path, "rb") as f:
    mean = pickle.load(f)
    print("mean=" + str(mean))
    x = pickle.load(f)
    y = pickle.load(f)

model = keras.models.load_model(model_path)

print(model.summary())

x = np.array(x)
y = np.array(y)

result = model.evaluate(x,y)

print("Evaluation result")
print("=================")

print("loss: {} precision: {}".format(result[0], result[1]))


import argparse
import pickle
import tensorflow.keras as keras
import numpy as np
import sys
import cv2

parser = argparse.ArgumentParser(description='Train the network given ')

parser.add_argument('-b', '--database-path', dest='imgdb_path',
                    help='Path to the image database containing test data.'
                         'Default is img.db in current folder.')
parser.add_argument('-m', '--model-path', dest='model_path',
                    help='Store the trained model using this path. Default is model.h5.')

args = parser.parse_args()

imgdb_path = "img.db"
model_path = "model.h5"
res = {"x": 16, "y": 16}

if args.model_path is not None:
    model_path = args.model_path

if args.imgdb_path is not None:
    imgdb_path = args.imgdb_path

with open(imgdb_path, "rb") as f:
    mean = pickle.load(f)
    print("mean=" + str(mean))
    x = pickle.load(f)
    y = pickle.load(f)

model = keras.models.load_model(model_path)

print(model.summary())

x = np.array(x)
y = np.array(y)

result = model.evaluate(x,y)

print("Evaluation result")
print("=================")

for idx in range(0, len(result)):
    print(model.metrics_names[idx] + ":", result[idx])


Print all evaluation metrics associated with the model
Print all evaluation metrics associated with the model

  
  import argparse
  import pickle
  import tensorflow.keras as keras
  import numpy as np
  import sys
  import cv2
  
  parser = argparse.ArgumentParser(description='Train the network given ')
  
  parser.add_argument('-b', '--database-path', dest='imgdb_path',
                      help='Path to the image database containing test data.'
                           'Default is img.db in current folder.')
  parser.add_argument('-m', '--model-path', dest='model_path',
                      help='Store the trained model using this path. Default is model.h5.')
  
  args = parser.parse_args()
  
  imgdb_path = "img.db"
  model_path = "model.h5"
  res = {"x": 16, "y": 16}
  
  if args.model_path is not None:
      model_path = args.model_path
  
  if args.imgdb_path is not None:
      imgdb_path = args.imgdb_path
  
  with open(imgdb_path, "rb") as f:
      mean = pickle.load(f)
      print("mean=" + str(mean))
      x = pickle.load(f)
      y = pickle.load(f)
  
  model = keras.models.load_model(model_path)
  
  print(model.summary())
  
  x = np.array(x)
  y = np.array(y)
  
  result = model.evaluate(x,y)
  
  print("Evaluation result")
  print("=================")
  
- print("loss: {} precision: {}".format(result[0], result[1]))
+ for idx in range(0, len(result)):
+     print(model.metrics_names[idx] + ":", result[idx])
  
+ 
Print all evaluation metrics associated with the model
## Code Before:

import argparse
import pickle
import tensorflow.keras as keras
import numpy as np
import sys
import cv2

parser = argparse.ArgumentParser(description='Train the network given ')

parser.add_argument('-b', '--database-path', dest='imgdb_path',
                    help='Path to the image database containing test data.'
                         'Default is img.db in current folder.')
parser.add_argument('-m', '--model-path', dest='model_path',
                    help='Store the trained model using this path. Default is model.h5.')

args = parser.parse_args()

imgdb_path = "img.db"
model_path = "model.h5"
res = {"x": 16, "y": 16}

if args.model_path is not None:
    model_path = args.model_path

if args.imgdb_path is not None:
    imgdb_path = args.imgdb_path

with open(imgdb_path, "rb") as f:
    mean = pickle.load(f)
    print("mean=" + str(mean))
    x = pickle.load(f)
    y = pickle.load(f)

model = keras.models.load_model(model_path)

print(model.summary())

x = np.array(x)
y = np.array(y)

result = model.evaluate(x,y)

print("Evaluation result")
print("=================")

print("loss: {} precision: {}".format(result[0], result[1]))

## Instruction:
Print all evaluation metrics associated with the model
## Code After:

import argparse
import pickle
import tensorflow.keras as keras
import numpy as np
import sys
import cv2

parser = argparse.ArgumentParser(description='Train the network given ')

parser.add_argument('-b', '--database-path', dest='imgdb_path',
                    help='Path to the image database containing test data.'
                         'Default is img.db in current folder.')
parser.add_argument('-m', '--model-path', dest='model_path',
                    help='Store the trained model using this path. Default is model.h5.')

args = parser.parse_args()

imgdb_path = "img.db"
model_path = "model.h5"
res = {"x": 16, "y": 16}

if args.model_path is not None:
    model_path = args.model_path

if args.imgdb_path is not None:
    imgdb_path = args.imgdb_path

with open(imgdb_path, "rb") as f:
    mean = pickle.load(f)
    print("mean=" + str(mean))
    x = pickle.load(f)
    y = pickle.load(f)

model = keras.models.load_model(model_path)

print(model.summary())

x = np.array(x)
y = np.array(y)

result = model.evaluate(x,y)

print("Evaluation result")
print("=================")

for idx in range(0, len(result)):
    print(model.metrics_names[idx] + ":", result[idx])

