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Hi, I am using Tensorflow to train a neural network ( The neural network doesn't contain any variables ). This is my neural network graph in Tensorflow.

X = tf.placeholder(tf.float32, [None,training_set.shape[1]],name = 'X') Y = tf.placeholder(tf.float32,[None,training_labels.shape[1]], name = 'Y')

A1 = tf.contrib.layers.fully_connected(X, num_outputs = 50, activation_fn = tf.nn.relu) A1 = tf.nn.dropout(A1, 0.8) A2 = tf.contrib.layers.fully_connected(A1, num_outputs = 2, activation_fn = None)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = A2, labels = Y)) global_step = tf.Variable(0, trainable=False) start_learning_rate = 0.001 learning_rate = tf.train.exponential_decay(start_learning_rate, global_step, 100, 0.1, True ) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

I wanted to know how this graph should be saved in tensorflow so as to load it using readNetFromTensorflow

Hi, I am using Tensorflow to train a neural network ( The neural network doesn't contain any variables ). This is my neural network graph in Tensorflow.

X = tf.placeholder(tf.float32, [None,training_set.shape[1]],name = 'X')
Y = tf.placeholder(tf.float32,[None,training_labels.shape[1]], name = 'Y')
'Y')
  
A1 = tf.contrib.layers.fully_connected(X, num_outputs = 50, activation_fn = tf.nn.relu)
A1 = tf.nn.dropout(A1, 0.8)
A2 = tf.contrib.layers.fully_connected(A1, num_outputs = 2, activation_fn = None)None)
  
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = A2, labels = Y))
global_step = tf.Variable(0, trainable=False)
start_learning_rate = 0.001
learning_rate = tf.train.exponential_decay(start_learning_rate, global_step, 100, 0.1, True )
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)