我正在嘗試使用示例代碼從 Tensorflowhub 加載模型。它與 FP32 完美搭配。一旦我添加 tf.keras.mixed_precision.set_global_policy('mixed_float16') 以啟用混合浮點數，它就會引發錯誤。看起來像尺寸問題，但它適用于 FP32。這是可重現的代碼：
import tensorflow as tf
import tensorflow_hub as hub
IMAGE_SIZE = (224,224)
class_names = ['cat','dog']
#If you comment out the following line, the code works fine.
tf.keras.mixed_precision.set_global_policy('mixed_float16')
# --------
model_handle = "https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/5"
do_fine_tuning = False
print("Building model with", model_handle)
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=IMAGE_SIZE (3,)),
hub.KerasLayer(model_handle, trainable=do_fine_tuning),
tf.keras.layers.Dropout(rate=0.2),
tf.keras.layers.Dense(len(class_names),
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
])
model.build((None,) IMAGE_SIZE (3,))
model.summary()
拋出以下錯誤：
Building model with https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [8], in <cell line: 4>()
2 do_fine_tuning = False
3 print("Building model with", model_handle)
----> 4 model = tf.keras.Sequential([
5 tf.keras.layers.InputLayer(input_shape=IMAGE_SIZE (3,)),
6 hub.KerasLayer(model_handle, trainable=do_fine_tuning),
7 tf.keras.layers.Dropout(rate=0.2),
8 tf.keras.layers.Dense(len(class_names),
9 kernel_regularizer=tf.keras.regularizers.l2(0.0001))
10 ])
11 model.build((None,) IMAGE_SIZE (3,))
12 model.summary()
File ~/miniconda3/envs/fahtx/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py:587, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs)
585 self._self_setattr_tracking = False # pylint: disable=protected-access
586 try:
--> 587 result = method(self, *args, **kwargs)
588 finally:
589 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
File ~/miniconda3/envs/fahtx/lib/python3.8/site-packages/keras/utils/traceback_utils.py:67, in filter_traceback.<locals>.error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
File /tmp/__autograph_generated_fileo7avm3_o.py:74, in outer_factory.<locals>.inner_factory.<locals>.tf__call(self, inputs, training)
72 result = ag__.converted_call(ag__.ld(smart_cond).smart_cond, (ag__.ld(training), ag__.autograph_artifact((lambda : ag__.converted_call(ag__.ld(f), (), dict(training=True), fscope))), ag__.autograph_artifact((lambda : ag__.converted_call(ag__.ld(f), (), dict(training=False), fscope)))), None, fscope)
73 result = ag__.Undefined('result')
---> 74 ag__.if_stmt(ag__.not_(ag__.ld(self)._has_training_argument), if_body_3, else_body_3, get_state_3, set_state_3, ('result', 'training'), 1)
76 def get_state_6():
77 return (result,)
File /tmp/__autograph_generated_fileo7avm3_o.py:72, in outer_factory.<locals>.inner_factory.<locals>.tf__call.<locals>.else_body_3()
70 training = False
71 ag__.if_stmt(ag__.ld(self).trainable, if_body_2, else_body_2, get_state_2, set_state_2, ('training',), 1)
---> 72 result = ag__.converted_call(ag__.ld(smart_cond).smart_cond, (ag__.ld(training), ag__.autograph_artifact((lambda : ag__.converted_call(ag__.ld(f), (), dict(training=True), fscope))), ag__.autograph_artifact((lambda : ag__.converted_call(ag__.ld(f), (), dict(training=False), fscope)))), None, fscope)
File /tmp/__autograph_generated_fileo7avm3_o.py:72, in outer_factory.<locals>.inner_factory.<locals>.tf__call.<locals>.else_body_3.<locals>.<lambda>()
70 training = False
71 ag__.if_stmt(ag__.ld(self).trainable, if_body_2, else_body_2, get_state_2, set_state_2, ('training',), 1)
---> 72 result = ag__.converted_call(ag__.ld(smart_cond).smart_cond, (ag__.ld(training), ag__.autograph_artifact((lambda : ag__.converted_call(ag__.ld(f), (), dict(training=True), fscope))), ag__.autograph_artifact((lambda : ag__.converted_call(ag__.ld(f), (), dict(training=False), fscope)))), None, fscope)
ValueError: Exception encountered when calling layer "keras_layer_3" (type KerasLayer).
in user code:
File "/root/miniconda3/envs/fahtx/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 237, in call *
result = smart_cond.smart_cond(training,
ValueError: Could not find matching concrete function to call loaded from the SavedModel. Got:
Positional arguments (4 total):
* <tf.Tensor 'inputs:0' shape=(None, 224, 224, 3) dtype=float16>
* False
* False
* 0.99
Keyword arguments: {}
Expected these arguments to match one of the following 4 option(s):
Option 1:
Positional arguments (4 total):
* TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name='inputs')
* True
* True
* TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
Keyword arguments: {}
Option 2:
Positional arguments (4 total):
* TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name='inputs')
* True
* False
* TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
Keyword arguments: {}
Option 3:
Positional arguments (4 total):
* TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name='inputs')
* False
* True
* TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
Keyword arguments: {}
Option 4:
Positional arguments (4 total):
* TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name='inputs')
* False
* False
* TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
Keyword arguments: {}
Call arguments received by layer "keras_layer_3" (type KerasLayer):
? inputs=tf.Tensor(shape=(None, 224, 224, 3), dtype=float16)
? training=False
uj5u.com熱心網友回復：
當 float16 被啟用作為變數規則時，它是關于目標“dtype”的，它試圖使用 float16，那么您只需將 float32 指定為所需的模型輸入。在將顏色切換為轉換時，我喜歡將通道號作為影像屬性包含在內。某些功能無需通道編號即可作業，但對于這些功能，您需要一種翻譯方法。樣本調整大小（）-> img_to_array（）-> 預測（）或邊界框作業。
[ 樣本 ]：
import tensorflow as tf
import tensorflow_hub as hub
IMAGE_SIZE = ( 224,224,3 )
class_names = ['cat','dog']
tf.keras.mixed_precision.set_global_policy('mixed_float16')
model_handle = "https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/5"
do_fine_tuning = False
print("Building model with", model_handle)
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=IMAGE_SIZE, dtype=tf.float32),
hub.KerasLayer(model_handle, trainable=do_fine_tuning , dtype=tf.float32),
tf.keras.layers.Dropout(rate=0.2),
tf.keras.layers.Dense(len(class_names),
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
])
model.build((None,) IMAGE_SIZE)
model.summary()
[ 錯誤 ]：
ValueError: Could not find matching concrete function to call loaded from the SavedModel. Got:
Positional arguments (4 total):
* <tf.Tensor 'inputs:0' shape=(None, 224, 224, 3) dtype=float16>
* False
* False
* 0.99
Keyword arguments: {}
[ 輸出 ]：
F:\temp\Python>python tf_test_mixed_float16.py
WARNING:tensorflow:Mixed precision compatibility check (mixed_float16): WARNING
Your GPU may run slowly with dtype policy mixed_float16 because it does not have compute capability of at least 7.0. Your GPU:
NVIDIA GeForce GTX 1060 6GB, compute capability 6.1
See https://developer.nvidia.com/cuda-gpus for a list of GPUs and their compute capabilities.
If you will use compatible GPU(s) not attached to this host, e.g. by running a multi-worker model, you can ignore this warning. This message will only be logged once
Building model with https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/5
2022-06-17 15:02:41.319205: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-06-17 15:02:41.878364: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4632 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
keras_layer (KerasLayer) (None, 2048) 23561152
dropout (Dropout) (None, 2048) 0
dense (Dense) (None, 2) 4098
=================================================================
Total params: 23,565,250
Trainable params: 4,098
Non-trainable params: 23,561,152
_________________________________________________________________
F:\temp\Python>
轉載請註明出處，本文鏈接：https://www.uj5u.com/houduan/493875.html
上一篇：使用tensorflowkeras訓練模型時，如何每10個epoch列印一個日志行？
下一篇：如何在keras中編輯功能模型？