本文整理汇总了Python中tensorflow.lite.python.interpreter.Interpreter.set_tensor方法的典型用法代码示例。如果您正苦于以下问题：Python Interpreter.set_tensor方法的具体用法？Python Interpreter.set_tensor怎么用？Python Interpreter.set_tensor使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.lite.python.interpreter.Interpreter的用法示例。
在下文中一共展示了Interpreter.set_tensor方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testFunctionalSequentialModel
# 需要导入模块: from tensorflow.lite.python.interpreter import Interpreter [as 别名]
# 或者: from tensorflow.lite.python.interpreter.Interpreter import set_tensor [as 别名]
def testFunctionalSequentialModel(self):
"""Test a Functional tf.keras model containing a Sequential model."""
with session.Session().as_default():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model = keras.models.Model(model.input, model.output)
model.compile(
loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(),
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
model.predict(x)
model.predict(x)
fd, keras_file = tempfile.mkstemp('.h5')
try:
keras.models.save_model(model, keras_file)
finally:
os.close(fd)
# Convert to TFLite model.
converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
tflite_model = converter.convert()
self.assertTrue(tflite_model)
# Check tensor details of converted model.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(1, len(input_details))
self.assertEqual('dense_input', input_details[0]['name'])
self.assertEqual(np.float32, input_details[0]['dtype'])
self.assertTrue(([1, 3] == input_details[0]['shape']).all())
self.assertEqual((0., 0.), input_details[0]['quantization'])
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual('time_distributed/Reshape_1', output_details[0]['name'])
self.assertEqual(np.float32, output_details[0]['dtype'])
self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all())
self.assertEqual((0., 0.), output_details[0]['quantization'])
# Check inference of converted model.
input_data = np.array([[1, 2, 3]], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
tflite_result = interpreter.get_tensor(output_details[0]['index'])
keras_model = keras.models.load_model(keras_file)
keras_result = keras_model.predict(input_data)
np.testing.assert_almost_equal(tflite_result, keras_result, 5)
os.remove(keras_file)示例2: _evaluateTFLiteModel
# 需要导入模块: from tensorflow.lite.python.interpreter import Interpreter [as 别名]
# 或者: from tensorflow.lite.python.interpreter.Interpreter import set_tensor [as 别名]
def _evaluateTFLiteModel(self, tflite_model, input_data):
"""Evaluates the model on the `input_data`."""
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
for input_tensor, tensor_data in zip(input_details, input_data):
interpreter.set_tensor(input_tensor['index'], tensor_data.numpy())
interpreter.invoke()
return interpreter.get_tensor(output_details[0]['index'])示例3: testSequentialModel
# 需要导入模块: from tensorflow.lite.python.interpreter import Interpreter [as 别名]
# 或者: from tensorflow.lite.python.interpreter.Interpreter import set_tensor [as 别名]
def testSequentialModel(self):
"""Test a Sequential tf.keras model with default inputs."""
keras_file = self._getSequentialModel()
converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
tflite_model = converter.convert()
self.assertTrue(tflite_model)
# Check tensor details of converted model.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(1, len(input_details))
self.assertEqual('dense_input', input_details[0]['name'])
self.assertEqual(np.float32, input_details[0]['dtype'])
self.assertTrue(([1, 3] == input_details[0]['shape']).all())
self.assertEqual((0., 0.), input_details[0]['quantization'])
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual('time_distributed/Reshape_1', output_details[0]['name'])
self.assertEqual(np.float32, output_details[0]['dtype'])
self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all())
self.assertEqual((0., 0.), output_details[0]['quantization'])
# Check inference of converted model.
input_data = np.array([[1, 2, 3]], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
tflite_result = interpreter.get_tensor(output_details[0]['index'])
keras_model = keras.models.load_model(keras_file)
keras_result = keras_model.predict(input_data)
np.testing.assert_almost_equal(tflite_result, keras_result, 5)
os.remove(keras_file)