I have a DQN all set up and working, but I can't figure out how to display the loss without leaving the Tensorflow session.
I first thought it involved creating a new function or class, but I'm not sure where to put it in the code, and what specifically to put into the function or class.
observations = tf.placeholder(tf.float32, shape=[None, num_stops], name='observations')
actions = tf.placeholder(tf.int32,shape=[None], name='actions')
rewards = tf.placeholder(tf.float32,shape=[None], name='rewards')
# Model
Y = tf.layers.dense(observations, 200, activation=tf.nn.relu)
Ylogits = tf.layers.dense(Y, num_stops)
# sample an action from predicted probabilities
sample_op = tf.random.categorical(logits=Ylogits, num_samples=1)
# loss
cross_entropies = tf.losses.softmax_cross_entropy(onehot_labels=tf.one_hot(actions,num_stops), logits=Ylogits)
loss = tf.reduce_sum(rewards * cross_entropies)
# training operation
optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001, decay=.99)
train_op = optimizer.minimize(loss)
I then run the network, which works without error.
with tf.Session() as sess:
'''etc. The network is run'''
sess.run(train_op, feed_dict={observations: observations_list,
actions: actions_list,
rewards: rewards_list})
I want to have loss from train_op displayed to the user.
JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)