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>".