Tesorflow Apprendre en profondeur à partir de CSV ... problème avec les gradients

Question

Je suis nouveau sur Tensorflow. Après avoir regardé la documentation de tf, plusieurs tutoriels, et les questions StackOverflow, je n'arrive pas à trouver de réponse, pour le moment. Je lis dans les caractéristiques et l'étiquette d'un fichier CSV.

import tensorflow as tf 

input_nodes = 13 


n_nodes_hl1 = 25 
n_nodes_hl2 = 25 
n_nodes_hl3 = 25 

n_classes = 1 

x = tf.placeholder('float', [None, input_nodes]) 
y = tf.placeholder('float') 


def neural_network_model(data): 
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([input_nodes,n_nodes_hl1])), 
         'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))} 

    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])), 
         'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))} 

    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3])), 
         'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))} 

    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])), 
         'biases': tf.Variable(tf.random_normal([n_classes]))} 

    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases']) 
    l1 = tf.nn.relu(l1) 

    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases']) 
    l2 = tf.nn.relu(l2) 

    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases']) 
    l3 = tf.nn.relu(l3) 

    output = tf.matmul(l3, output_layer['weights']) + output_layer['biases'] 

    return output 

def train_neural_network(x): 

    #Obtain data from CSV File 
    #Training data looks like this... The first 13 columns are inputs, the last column is the result 
    #1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 
    #0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 
    train_filename_queue = tf.train.string_input_producer(["training_data.csv"]) 
    reader = tf.TextLineReader() 
    key, value = reader.read(train_filename_queue) 

    #Set defaults 
    record_defaults = [[1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]] 
    col01, col02, col03, col04, col05, col06, col07, col08, col09, col10, col11, col12, col13, outcome = tf.decode_csv(value, record_defaults=record_defaults) 
    features = tf.stack([col01, col02, col03, col04, col05, col06, col07, col08, col09, col10, col11, col12, col13]) 
    outputs = tf.stack([outcome]) 


    prediction = neural_network_model(x) 
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=prediction, logits=y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) #this line throws the error below 

    hm_epochs = 10 

    with tf.Session() as sess: 
     sess.run(tf.global_variables_initializer()) 

     coord = tf.train.Coordinator() 
     threads = tf.train.start_queue_runners(coord=coord) 
     for epoch in range(hm_epochs): 
      epoch_loss = 0 
      for _ in range(2): 
       _, c = sess.run([optimizer], feed_dict= {x: features, y: outputs}) 
       epoch_loss += c 
      print('Epoch', epoch + 1, 'completed out of', hm_epochs, 'loss', epoch_loss) 

     coord.request_stop() 
     coord.join(threads) 

print('Beginning to Train') 
train_neural_network(x) 
print('Done') 

Voici l'erreur que je reçois:

ValueError: No gradients provided for any variable, check your graph for ops that do not support gradients, between variables ["<tf.Variable 'Variable:0' shape=... 

Alors, voici ma question, suis-je mieux définir mon propre algorithme coût/optimiseur ou suis-je manque tout simplement un moyen simple d'ajouter des gradients aux valeurs existantes? Si la façon d'ajouter des dégradés me manque, pouvez-vous me diriger dans la bonne direction?

Answer 1

Thy variables locales d'initialisation ainsi:

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    sess.run(tf.local_variables_initializer())