I am using tf.estimator.Estimator to train a model:
def model_fn(features, labels, mode, params, config):
input_image = features["input_image"]
eval_metric_ops = {}
predictions = {}
# Create model
with tf.name_scope('Model'):
W = tf.Variable(tf.zeros([784, 10]), name="W")
b = tf.Variable(tf.zeros([10]), name="b")
logits = tf.nn.softmax(tf.matmul(input_image, W, name="MATMUL") + b, name="logits")
loss = None
train_op = None
if mode != tf.estimator.ModeKeys.PREDICT:
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))
train_op = tf.contrib.layers.optimize_loss(loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=params["learning_rate"],
optimizer=params["optimizer"])
# Add prediction
classes = tf.as_string(tf.argmax(input=logits, axis=1, name="class"))
with tf.name_scope('Predictions'):
predictions["logits"] = logits
predictions["classes"] = classes
export_outputs = {"classes": tf.estimator.export.ClassificationOutput(classes=classes)}
export_outputs = {"classes": tf.estimator.export.PredictOutput({"labels": classes})}
spec = tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
export_outputs=export_outputs,
training_chief_hooks=None,
training_hooks=None,
scaffold=None)
return spec
def input_fn(dataset, n=10):
return dataset.images[:n], dataset.labels[:n]
model_params = {"learning_rate": 1e-3,
"optimizer": "Adam"}
#run_path = os.path.join(runs_path, datetime.now().strftime("%Y-%m-%d-%H-%M-%S"))
run_path = os.path.join(runs_path, "run1")
if os.path.exists(run_path):
shutil.rmtree(run_path)
estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=run_path, params=model_params)
# Train
inputs = lambda: input_fn(mnist.train, n=15)
estimator.train(input_fn=inputs, steps=1000)
Model and weights are correctly saved during training.
Now I want to reload the model + weights in another script in order to make predictions.
But I don't know how to specify the input because I have no reference to it in the model_fn function.
# Get some data to predict
input_data = mnist.test.images[:5]
tf.reset_default_graph()
run_path = os.path.join(runs_path, "run1")
# Load the model (graph)
input_checkpoint = os.path.join(run_path, "model.ckpt-1000")
saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
# Restore the weights
sess = tf.InteractiveSession()
saver.restore(sess, input_checkpoint)
graph = sess.graph
# Get the op to compute for prediction
predict_op = graph.get_operation_by_name("Predictions/class")
# predictions = sess.run(predict_op, feed_dict=????)
Here is what returns graph.get_collection("variables"):
[<tf.Variable 'global_step:0' shape=() dtype=int64_ref>,
<tf.Variable 'Model/W:0' shape=(784, 10) dtype=float32_ref>,
<tf.Variable 'Model/b:0' shape=(10,) dtype=float32_ref>,
<tf.Variable 'OptimizeLoss/learning_rate:0' shape=() dtype=float32_ref>,
<tf.Variable 'OptimizeLoss/beta1_power:0' shape=() dtype=float32_ref>,
<tf.Variable 'OptimizeLoss/beta2_power:0' shape=() dtype=float32_ref>,
<tf.Variable 'OptimizeLoss/Model/W/Adam:0' shape=(784, 10) dtype=float32_ref>,
<tf.Variable 'OptimizeLoss/Model/W/Adam_1:0' shape=(784, 10) dtype=float32_ref>,
<tf.Variable 'OptimizeLoss/Model/b/Adam:0' shape=(10,) dtype=float32_ref>,
<tf.Variable 'OptimizeLoss/Model/b/Adam_1:0' shape=(10,) dtype=float32_ref>]
Do I need to specify a tf.placeholder for the input? But then how Tensorflow knows the input should feed this specific placeholder?
Also if I specify something like features = tf.constant(features, name="input") at the beginning of the model, I can't use it because it's not a Tensor but an Operation.
EDIT
After more investigation, I have found that I need to save my model using the Estimator.export_savedmodel() method (and not re-using the automatically saved checkpoints during training with the estimator.
feature_spec = {"input_image": tf.placeholder(dtype=tf.float32, shape=[None, 784])}
input_receiver_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(feature_spec)
estimator.export_savedmodel(model_path, input_receiver_fn, as_text=True)
Then I tried to load the model and do prediction but I don't know how to feed the model with my numpy images:
preds = sess.run("class", feed_dict={"input_image": input_data})
And the excepted error:
/home/hadim/local/conda/envs/ws/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/hadim/local/conda/envs/ws/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
931 except Exception as e:
932 raise TypeError('Cannot interpret feed_dict key as Tensor: '
--> 933 + e.args[0])
934
935 if isinstance(subfeed_val, ops.Tensor):
TypeError: Cannot interpret feed_dict key as Tensor: The name 'input_image' looks like an (invalid) Operation name, not a Tensor. Tensor names must be of the form "<op_name>:<output_index>".
As for the TypeError, I solve it in this way.
First, name the placeholder:
feature_spec = {"input_image": tf.placeholder(dtype=tf.float32, shape=[None, 784], name='input_image')}
Then you can use it like this:
feed_dict={"input_image:0": input_data}
Hope it can help someone.
EDIT
In this question, afterestimator.export_savedmodel(...)
you can do prediction like this:
with tf.Session(graph=tf.Graph()) as sess:
meta_graph_def = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], model_path)
signature = meta_graph_def.signature_def
x_tensor_name = signature['classes'].inputs['input_image'].name
y_tensor_name = signature['classes'].outputs['labels'].name
x = sess.graph.get_tensor_by_name(x_tensor_name)
y = sess.graph.get_tensor_by_name(y_tensor_name)
predictions = sess.run(y, {x: mnist.test.images[:5]})
The name of your input Tensor probably is input_image:0.
You can list the signature of your saved model by calling:
print(estimator.signature_def[tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY])
That should list the expected input/output Tensors.
I predict successfully by using tensorflow.contrib.predictor:
from tensorflow.contrib import predictor
predict_fn = predictor.from_saved_model(
export_dir='model/1535012949', # your model path
signature_def_key='predict',
)
predictions = predict_fn({'examples': examples}) # FYI, rename to `input_image`
But I want to predict by session and tensors also, so that I can use the traning model with other languages. Expect some perfect answer!
I am working with tf.contrib.learn.Estimator. As I see, the syntax and method signatures are almost the same so I believe the differences are related to the several Tensorflow versions. So you can create Estimator as usual with something like
estimator = learn.Estimator(
model_fn=your_model,
model_dir="tmp",
config=tf.contrib.learn.RunConfig(
save_checkpoints_steps=10,
save_summary_steps=10,
save_checkpoints_secs=None
)
)
Then you do the train asestimator.fit(input_fn=input_function, steps=100)
And then you can do the prediction calling
estimator .predict(prediction)
Mote, there is a trick, related with the Tensorflow's known issue. Calling predict does not properly initialize the Estimator, so you need to call
estimator.evaluate(x=prediction, y=label_array, steps=1)
before calling predict.
Hope, this helps.
来源：https://stackoverflow.com/questions/43966073/prediction-from-model-saved-with-tf-estimator-estimator-in-tensorflow