How to use the model after training in a tensor flow (save / load graph)

Question

My tensorflow version is 0.11. I want to save the schedule after a workout or save something else that can load its loador.

I / Using MetaGraph Export and Import

I already read this post: Tensorflow: how to save / restore the model?

My Save.py File :

X = tf.placeholder("float", [None, 28, 28, 1], name='X') Y = tf.placeholder("float", [None, 10], name='Y') tf.train.Saver() with tf.Session() as sess: ...run something ... final_tensor = tf.nn.softmax(py_x, name='final_result') tf.add_to_collection("final_tensor", final_tensor) predict_op = tf.argmax(py_x, 1) tf.add_to_collection("predict_op", predict_op) saver.save(sess, 'my_project') 

Then I ran load.py:

 with tf.Session() as sess: new_saver = tf.train.import_meta_graph('my_project.meta') new_saver.restore(sess, 'my_project') predict_op = tf.get_collection("predict_op")[0] for i in range(2): test_indices = np.arange(len(teX)) # Get A Test Batch np.random.shuffle(test_indices) test_indices = test_indices[0:test_size] print(i, np.mean(np.argmax(teY[test_indices], axis=1) == sess.run(predict_op, feed_dict={"X:0": teX[test_indices], "p_keep_conv:0": 1.0, "p_keep_hidden:0": 1.0}))) 

but it returns an error

 Traceback (most recent call last): File "load_05_convolution.py", line 62, in <module> "p_keep_hidden:0": 1.0}))) File "/home/khoa/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 717, in run run_metadata_ptr) File "/home/khoa/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 894, in _run % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape()))) ValueError: Cannot feed value of shape (256, 784) for Tensor u'X:0', which has shape '(?, 28, 28, 1)' 

I really don't know why?

If I add final_tensor = tf.get_collection("final_result")[0]

It returns another error:

 Traceback (most recent call last): File "load_05_convolution.py", line 46, in <module> final_tensor = tf.get_collection("final_result")[0] IndexError: list index out of range 

Is it because tf.add_to_collection contains only one place holder?

II / using tf.train.write_graph

I add this line to the end of save.py tf.train.write_graph(graph, 'folder', 'train.pb')

He successfully created the file 'train.pb'

My load.py :

 with tf.gfile.FastGFile('folder/train.pb', 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(graph_def, name='') with tf.Session() as sess: predict_op = sess.graph.get_tensor_by_name('predict_op:0') for i in range(2): test_indices = np.arange(len(teX)) # Get A Test Batch np.random.shuffle(test_indices) test_indices = test_indices[0:test_size] print(i, np.mean(np.argmax(teY[test_indices], axis=1) == sess.run(predict_op, feed_dict={"X:0": teX[test_indices], "p_keep_conv:0": 1.0, "p_keep_hidden:0": 1.0}))) 

Then it returns an error:

 Traceback (most recent call last): File "load_05_convolution.py", line 22, in <module> graph_def.ParseFromString(f.read()) File "/home/khoa/tensorflow/lib/python2.7/site-packages/google/protobuf/message.py", line 185, in ParseFromString self.MergeFromString(serialized) File "/home/khoa/tensorflow/lib/python2.7/site-packages/google/protobuf/internal/python_message.py", line 1085, in MergeFromString raise message_mod.DecodeError('Unexpected end-group tag.') google.protobuf.message.DecodeError: Unexpected end-group tag. 

Could you share the standard way, code or tutorial for saving / loading the model? I'm really confused.

Answer 1

Your first solution (using MetaGraph) almost works, but the error occurs because you load a batch of flattened MNIST training examples into tf.placeholder() , which expects the MNIST training series to be a four-dimensional tensor with the form batch_size x height (= 28) x width (= 28) x channels (= 1). The easiest way to solve this problem is to change the source data. Instead of this statement:

 print(i, np.mean(np.argmax(teY[test_indices], axis=1) == sess.run(predict_op, feed_dict={ "X:0": teX[test_indices], "p_keep_conv:0": 1.0, "p_keep_hidden:0": 1.0}))) 

... try the following statement, which modifies your input accordingly:

 print(i, np.mean(np.argmax(teY[test_indices], axis=1) == sess.run(predict_op, feed_dict={ "X:0": teX[test_indices].reshape(-1, 28, 28, 1), "p_keep_conv:0": 1.0, "p_keep_hidden:0": 1.0})))