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We get the iterator's generated tensor, called input_tensor which will serve as input to our model. From them, we create the Dataset and then its Iterator.
Critical ops trainer 2.6 code#
The following code generates random data for the sake of the demonstration.
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Prediction = restored_graph.get_tensor_by_name('dense/BiasAdd:0') Labels_placeholder = graph.get_tensor_by_name('labels_placeholder:0') Sess, 'path/to/your/location/', inputs, outputsīatch_size_placeholder = graph.get_tensor_by_name('batch_size_placeholder:0')įeatures_placeholder = graph.get_tensor_by_name('features_placeholder:0') "labels_placeholder": labels_placeholder, "features_placeholder": features_placeholder, "batch_size_placeholder": batch_size_placeholder, Python 3 Tensorflow 1.14 import tensorflow as tfįrom tensorflow.saved_model import tag_constants Also a standalone code example using the tf.data.Dataset API. Many good answer, for completeness I'll add my 2 cents: simple_save. # Later, launch the model, use the saver to restore variables from disk, and Print("Model saved in path: %s" % save_path) Save_path = saver.save(sess, "/tmp/model.ckpt") # Later, launch the model, initialize the variables, do some work, and save the # Add ops to save and restore all the variables. Init_op = tf.global_variables_initializer() V2 = tf.get_variable("v2", shape=, initializer = tf.zeros_initializer) V1 = tf.get_variable("v1", shape=, initializer = tf.zeros_initializer) TensorFlow APIs).įrom the docs: Save # Create some variables. They are thus suitable for deployment via TensorFlow Serving, TensorFlow Lite, TensorFlow.js, or programs in other programming languages (the C, C++, Java, Go, Rust, C# etc. Models in this format are independent of the source code that created the model. The SavedModel format on the other hand includes a serialized description of the computation defined by the model in addition to the parameter values (checkpoint). Checkpoints do not contain any description of the computation defined by the model and thus are typically only useful when source code that will use the saved parameter values is available. Print("Restored from ".format(manager.latest_checkpoint))Įxhaustive and useful tutorial on saved_model -> Ĭheckpoints capture the exact value of all parameters (tf.Variable objects) used by a model. Manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3) Step=tf.Variable(1), optimizer=opt, net=net, iterator=iterator Optimizer.apply_gradients(zip(gradients, variables)) Loss = tf.reduce_mean(tf.abs(output - example)) """Trains `net` on `example` using `optimizer`.""" Tf._tensor_slices(dict(x=inputs, y=labels)).repeat().batch(2) This and some more advanced use-cases have been explained very well here.Ī quick complete tutorial to save and restore Tensorflow modelsĪdapted from the docs #. Op_to_restore = graph.get_tensor_by_name("op_to_restore:0")
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#Now, access the op that you want to run. # Now, let's access and create placeholders variables and # This will print 2, which is the value of bias that we saved Saver.restore(sess,tf.train.latest_checkpoint('./')) Saver = tf.train.import_meta_graph('my_test_ta') #First let's load meta graph and restore weights Restore the model: import tensorflow as tf Saver.save(sess, 'my_test_model',global_step=1000) #Create a saver object which will save all the variables
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W4 = tf.multiply(w3,b1,name="op_to_restore") #Define a test operation that we will restore I am improving my answer to add more details for saving and restoring models.