如何使用 TensorFlow 正确创建估算器
How to create correctly an estimator with TensorFlow
我想用 Python 创建一个神经网络，但我在估算器方面遇到了一些问题。
首先，我阅读了一些 documentation 有关估算器规范的内容，我认为我正确地创建了估算器类型：
estimate_train = tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
estimate_test = tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.EVAL, loss=loss)
但是当我想创建将用于训练我的网络的估计器时：
estimator_ = tf.estimator.Estimator(model_fn= estimate_train, model_dir="Path")
出现以下错误：
TypeError Traceback (most recent call last)
/usr/lib/python3.5/inspect.py in getfullargspec(func)
1088 skip_bound_arg=False,
-> 1089 sigcls=Signature)
1090 except Exception as ex:
/usr/lib/python3.5/inspect.py in _signature_from_callable(obj, follow_wrapper_chains, skip_bound_arg, sigcls)
2155 if not callable(obj):
-> 2156 raise TypeError('{!r} is not a callable object'.format(obj))
2157
TypeError: EstimatorSpec(mode='eval', predictions={}, loss=<tf.Tensor 'mean_squared_error/value:0' shape=() dtype=float32>, train_op=None, eval_metric_ops={}, export_outputs=None, training_chief_hooks=(), training_hooks=(), scaffold=<tensorflow.python.training.monitored_session.Scaffold object at 0x7f9ddc083748>, evaluation_hooks=(), prediction_hooks=()) is not a callable object
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
<ipython-input-2-c86a69b4da46> in <module>()
39
40
---> 41 estimatorn = tf.estimator.Estimator(model_fn= estimate_test, model_dir="/home/jabou/Bureau")
/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py in __init__(self, model_fn, model_dir, config, params, warm_start_from)
221 if model_fn is None:
222 raise ValueError('model_fn must be provided to Estimator.')
--> 223 _verify_model_fn_args(model_fn, params)
224 self._model_fn = model_fn
225 self._params = copy.deepcopy(params or {})
/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py in _verify_model_fn_args(model_fn, params)
1212 def _verify_model_fn_args(model_fn, params):
1213 """Verifies model fn arguments."""
-> 1214 args = set(util.fn_args(model_fn))
1215 if 'features' not in args:
1216 raise ValueError('model_fn (%s) must include features argument.' % model_fn)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/util.py in fn_args(fn)
58 if _is_callable_object(fn):
59 fn = fn.__call__
---> 60 args = tf_inspect.getfullargspec(fn).args
61 if _is_bounded_method(fn):
62 args.remove('self')
/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_inspect.py in getfullargspec(obj)
88 decorators, target = tf_decorator.unwrap(obj)
89 return next((d.decorator_argspec for d in decorators
---> 90 if d.decorator_argspec is not None), spec_fn(target))
91
92
/usr/lib/python3.5/inspect.py in getfullargspec(func)
1093 # else. So to be fully backwards compatible, we catch all
1094 # possible exceptions here, and reraise a TypeError.
-> 1095 raise TypeError('unsupported callable') from ex
1096
1097 args = []
TypeError: unsupported callable
这是我的完整代码：
import tensorflow as tf
sess = tf.Session()
tf.reset_default_graph()
batch_size = 20
# Values needed to create the network
input_ = tf.placeholder(tf.float32, shape=(batch_size, 1 , 1 ,1))
filter_ = tf.placeholder(tf.float32, shape=(batch_size, 1 , 2700 ,1))
output_network = tf.placeholder(tf.int32, shape=(4,))
output_real = tf.placeholder(tf.float32)
x_var = tf.get_variable(name = 'x_var', dtype = tf.float32, initializer = tf.random_normal((batch_size,1,1,1), 0, 0.001)) # Initialised values
bias = tf.Variable(tf.zeros([2700]))
# Network
logits = tf.nn.conv2d_transpose(x_var, filter_ ,output_network,[1,1,3,1],'SAME') + bias
loss = tf.losses.mean_squared_error(output_real,logits) # loss function
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss=loss,global_step=tf.train.get_global_step())
# Estimators specification
estimate_train = tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
estimate_test = tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.EVAL, loss=loss)
# Estimator
estimator_ = tf.estimator.Estimator(model_fn= estimate_test, model_dir="Path")
你能帮帮我吗？
编辑
根据@f4的回答，我修改了我的代码，但我仍然有同样的错误：
import tensorflow as tf
sess = tf.Session()
tf.reset_default_graph()
batch_size = 20
def model(param):
# Values needed to create the network
input_ = tf.placeholder(tf.float32, shape=(batch_size, 1 , 1 ,1))
filter_ = tf.placeholder(tf.float32, shape=(batch_size, 1 , 2700 ,1))
output_network = tf.placeholder(tf.int32, shape=(4,))
output_real = tf.placeholder(tf.float32)
x_var = tf.get_variable(name = 'x_var', dtype = tf.float32, initializer = tf.random_normal((batch_size,1,1,1), 0, 0.001)) # Initialised values
bias = tf.Variable(tf.zeros([2700]))
# Network
logits = tf.nn.conv2d_transpose(x_var, filter_ ,output_network,[1,1,3,1],'SAME') + bias
loss = tf.losses.mean_squared_error(output_real,logits) # loss function
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss=loss,global_step=tf.train.get_global_step())
# Estimators specification
if param == "train":
return tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
if param == "test":
return tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.EVAL, loss=loss)
# Estimator
estimator_ = tf.estimator.Estimator(model_fn= model("train"), model_dir="Path")
又怎么了？
您直接给了 Estimator EstimatorSpec，但它不正确。
model_fn 应该是一个函数，它 return 是 EstimatorSpec 的一个实例。
稍后将调用此函数。结果它抱怨说你给的东西是不可调用的。
编辑
不用再给函数的 return 值了，您需要传递的是函数本身，它是可调用的：
estimator_ = tf.estimator.Estimator(model_fn = model, model_dir="Path")
你的model_fn也不好。我建议你阅读文档 https://www.tensorflow.org/get_started/custom_estimators
它应该有这个签名：
def my_model_fn(
features, # This is batch_features from input_fn
labels, # This is batch_labels from input_fn
mode, # An instance of tf.estimator.ModeKeys
params): # Additional configuration
The first two arguments are the batches of features and labels
returned from the input function; that is, features and labels are the
handles to the data your model will use. The mode argument indicates
whether the caller is requesting training, predicting, or evaluation.
您应该使用特征和标签而不是创建占位符。
此外，在您的 model_fn 中，您不应该在未处于训练模式时创建训练操作。
我想用 Python 创建一个神经网络，但我在估算器方面遇到了一些问题。
首先，我阅读了一些 documentation 有关估算器规范的内容，我认为我正确地创建了估算器类型：
estimate_train = tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
estimate_test = tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.EVAL, loss=loss)
但是当我想创建将用于训练我的网络的估计器时：
estimator_ = tf.estimator.Estimator(model_fn= estimate_train, model_dir="Path")
出现以下错误：
TypeError Traceback (most recent call last)
/usr/lib/python3.5/inspect.py in getfullargspec(func)
1088 skip_bound_arg=False,
-> 1089 sigcls=Signature)
1090 except Exception as ex:
/usr/lib/python3.5/inspect.py in _signature_from_callable(obj, follow_wrapper_chains, skip_bound_arg, sigcls)
2155 if not callable(obj):
-> 2156 raise TypeError('{!r} is not a callable object'.format(obj))
2157
TypeError: EstimatorSpec(mode='eval', predictions={}, loss=<tf.Tensor 'mean_squared_error/value:0' shape=() dtype=float32>, train_op=None, eval_metric_ops={}, export_outputs=None, training_chief_hooks=(), training_hooks=(), scaffold=<tensorflow.python.training.monitored_session.Scaffold object at 0x7f9ddc083748>, evaluation_hooks=(), prediction_hooks=()) is not a callable object
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
<ipython-input-2-c86a69b4da46> in <module>()
39
40
---> 41 estimatorn = tf.estimator.Estimator(model_fn= estimate_test, model_dir="/home/jabou/Bureau")
/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py in __init__(self, model_fn, model_dir, config, params, warm_start_from)
221 if model_fn is None:
222 raise ValueError('model_fn must be provided to Estimator.')
--> 223 _verify_model_fn_args(model_fn, params)
224 self._model_fn = model_fn
225 self._params = copy.deepcopy(params or {})
/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py in _verify_model_fn_args(model_fn, params)
1212 def _verify_model_fn_args(model_fn, params):
1213 """Verifies model fn arguments."""
-> 1214 args = set(util.fn_args(model_fn))
1215 if 'features' not in args:
1216 raise ValueError('model_fn (%s) must include features argument.' % model_fn)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/util.py in fn_args(fn)
58 if _is_callable_object(fn):
59 fn = fn.__call__
---> 60 args = tf_inspect.getfullargspec(fn).args
61 if _is_bounded_method(fn):
62 args.remove('self')
/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_inspect.py in getfullargspec(obj)
88 decorators, target = tf_decorator.unwrap(obj)
89 return next((d.decorator_argspec for d in decorators
---> 90 if d.decorator_argspec is not None), spec_fn(target))
91
92
/usr/lib/python3.5/inspect.py in getfullargspec(func)
1093 # else. So to be fully backwards compatible, we catch all
1094 # possible exceptions here, and reraise a TypeError.
-> 1095 raise TypeError('unsupported callable') from ex
1096
1097 args = []
TypeError: unsupported callable
这是我的完整代码：
import tensorflow as tf
sess = tf.Session()
tf.reset_default_graph()
batch_size = 20
# Values needed to create the network
input_ = tf.placeholder(tf.float32, shape=(batch_size, 1 , 1 ,1))
filter_ = tf.placeholder(tf.float32, shape=(batch_size, 1 , 2700 ,1))
output_network = tf.placeholder(tf.int32, shape=(4,))
output_real = tf.placeholder(tf.float32)
x_var = tf.get_variable(name = 'x_var', dtype = tf.float32, initializer = tf.random_normal((batch_size,1,1,1), 0, 0.001)) # Initialised values
bias = tf.Variable(tf.zeros([2700]))
# Network
logits = tf.nn.conv2d_transpose(x_var, filter_ ,output_network,[1,1,3,1],'SAME') + bias
loss = tf.losses.mean_squared_error(output_real,logits) # loss function
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss=loss,global_step=tf.train.get_global_step())
# Estimators specification
estimate_train = tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
estimate_test = tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.EVAL, loss=loss)
# Estimator
estimator_ = tf.estimator.Estimator(model_fn= estimate_test, model_dir="Path")
你能帮帮我吗？
编辑
根据@f4的回答，我修改了我的代码，但我仍然有同样的错误：
import tensorflow as tf
sess = tf.Session()
tf.reset_default_graph()
batch_size = 20
def model(param):
# Values needed to create the network
input_ = tf.placeholder(tf.float32, shape=(batch_size, 1 , 1 ,1))
filter_ = tf.placeholder(tf.float32, shape=(batch_size, 1 , 2700 ,1))
output_network = tf.placeholder(tf.int32, shape=(4,))
output_real = tf.placeholder(tf.float32)
x_var = tf.get_variable(name = 'x_var', dtype = tf.float32, initializer = tf.random_normal((batch_size,1,1,1), 0, 0.001)) # Initialised values
bias = tf.Variable(tf.zeros([2700]))
# Network
logits = tf.nn.conv2d_transpose(x_var, filter_ ,output_network,[1,1,3,1],'SAME') + bias
loss = tf.losses.mean_squared_error(output_real,logits) # loss function
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss=loss,global_step=tf.train.get_global_step())
# Estimators specification
if param == "train":
return tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
if param == "test":
return tf.estimator.EstimatorSpec(mode=tf.estimator.ModeKeys.EVAL, loss=loss)
# Estimator
estimator_ = tf.estimator.Estimator(model_fn= model("train"), model_dir="Path")
又怎么了？
您直接给了 Estimator EstimatorSpec，但它不正确。
model_fn 应该是一个函数，它 return 是 EstimatorSpec 的一个实例。
稍后将调用此函数。结果它抱怨说你给的东西是不可调用的。
编辑
不用再给函数的 return 值了，您需要传递的是函数本身，它是可调用的：
estimator_ = tf.estimator.Estimator(model_fn = model, model_dir="Path")
你的model_fn也不好。我建议你阅读文档 https://www.tensorflow.org/get_started/custom_estimators
它应该有这个签名：
def my_model_fn(
features, # This is batch_features from input_fn
labels, # This is batch_labels from input_fn
mode, # An instance of tf.estimator.ModeKeys
params): # Additional configuration
The first two arguments are the batches of features and labels returned from the input function; that is, features and labels are the handles to the data your model will use. The mode argument indicates whether the caller is requesting training, predicting, or evaluation.
您应该使用特征和标签而不是创建占位符。 此外，在您的 model_fn 中，您不应该在未处于训练模式时创建训练操作。