我想用Python创建一个神经网络，但估算器存在一些问题。
首先，我读了一些有关估算器规范的documentation，并且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)
但是当我想创建用于训练我的网络的估算器时：
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
这是我完整的代码：
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")
你能帮我吗？
编辑
在回答@ f4之后，我更正了我的代码，但仍然有相同的错误：
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？
答案 0 :(得分：1)
您直接给估算器设置了一个EstimatorSpec，这是不正确的。
model_fn应该是一个返回EstimatorSpec实例的函数。
稍后将调用此函数。结果，它抱怨您所提供的东西无法收回。
编辑
再也不是给函数的返回值，您需要传递的是函数本身，这是可以调用的：
estimator_ = tf.estimator.Estimator(model_fn = model, model_dir="Path")
您的model_fn也不好。建议您阅读文档https://www.tensorflow.org/get_started/custom_estimators
它应该具有以下签名：
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
前两个参数是功能部件和标签的批次 从输入函数返回；也就是说，功能和标签是 处理您的模型将使用的数据。模式参数指示 呼叫者是要求培训，预测还是评估。
您应该使用功能部件和标签，而不要创建临时所有者。 此外，在您的model_fn中，当您不处于训练模式时，不应创建训练操作。