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-
- import tensorflow as tf
- import math
-
- # Creates an inference graph.
- # Hidden 1
- images = tf.constant(1.2, tf.float32, shape=[100, 28])
- with tf.name_scope("hidden1"):
- weights = tf.Variable(
- tf.truncated_normal([28, 128],
- stddev=1.0 / math.sqrt(float(28))),
- name="weights")
- biases = tf.Variable(tf.zeros([128]),
- name="biases")
- hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
- # Hidden 2
- with tf.name_scope("hidden2"):
- weights = tf.Variable(
- tf.truncated_normal([128, 32],
- stddev=1.0 / math.sqrt(float(128))),
- name="weights")
- biases = tf.Variable(tf.zeros([32]),
- name="biases")
- hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
- # Linear
- with tf.name_scope("softmax_linear"):
- weights = tf.Variable(
- tf.truncated_normal([32, 10],
- stddev=1.0 / math.sqrt(float(32))),
- name="weights")
- biases = tf.Variable(tf.zeros([10]),
- name="biases")
- logits = tf.matmul(hidden2, weights) + biases
- tf.add_to_collection("logits", logits)
-
- init_all_op = tf.global_variables_initializer()
-
- with tf.Session() as sess:
- # Initializes all the variables.
- sess.run(init_all_op)
- # Runs to logit.
- sess.run(logits)
- # Creates a saver.
- saver0 = tf.train.Saver()
- saver0.save(sess, 'my-save-dir/my-model-10000')
- # Generates MetaGraphDef.
- saver0.export_meta_graph('my-save-dir/my-model-10000.meta')
-
-
- # Then later import it and extend it to a training graph.
- with tf.Session() as sess:
- new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
- new_saver.restore(sess, 'my-save-dir/my-model-10000')
- # Addes loss and train.
- labels = tf.constant(0, tf.int32, shape=[100], name="labels")
- batch_size = tf.size(labels)
- logits = tf.get_collection("logits")[0]
- loss = tf.losses.sparse_softmax_cross_entropy(labels=labels,
- logits=logits)
-
- tf.summary.scalar('loss', loss)
- # Creates the gradient descent optimizer with the given learning rate.
- optimizer = tf.train.GradientDescentOptimizer(0.01)
-
- # Runs train_op.
- train_op = optimizer.minimize(loss)
- sess.run(train_op)
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