python - cost function outputs 'nan' in tensorflow -


while studying tensorflow, faced problem.
cost function output 'nan'.

and, if find other wrong in source code let me know links it.

i trying send cost function value trained model, not working.

tf.reset_default_graph()  tf.set_random_seed(777)  x = tf.placeholder(tf.float32, [none, 20, 20, 3]) y = tf.placeholder(tf.float32, [none, 1])  tf.variable_scope('conv1') scope:     w1 = tf.variable(tf.random_normal([4, 4, 3, 32], stddev=0.01), name='weight1')           l1 = tf.nn.conv2d(x, w1, strides=[1, 1, 1, 1], padding='same')     l1 = tf.nn.relu(l1)     l1 = tf.nn.max_pool(l1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='same')     l1 = tf.reshape(l1, [-1, 10 * 10 * 32])      w1_hist = tf.summary.histogram('conv_weight1', w1)     l1_hist = tf.summary.histogram('conv_layer1', l1)  tf.name_scope('fully_connected_layer1') scope:     w2 = tf.get_variable('w2', shape=[10 * 10 * 32, 1], initializer=tf.contrib.layers.xavier_initializer())             b = tf.variable(tf.random_normal([1]))     hypothesis = tf.matmul(l1, w2) + b      w2_hist = tf.summary.histogram('fully_connected_weight1', w2)     b_hist = tf.summary.histogram('fully_connected_bias', b)     hypothesis_hist = tf.summary.histogram('hypothesis', hypothesis)  tf.name_scope('cost') scope:     cost = -tf.reduce_mean(y * tf.log(hypothesis) + (1 - y) * tf.log(1 - hypothesis))     cost_summary = tf.summary.scalar('cost', cost)  tf.name_scope('train_optimizer') scope:     optimizer = tf.train.adamoptimizer(learning_rate=0.0001).minimize(cost)    predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32) accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, y), dtype=tf.float32)) accuracy_summary = tf.summary.scalar('accuracy', accuracy)  train_data_batch, train_labels_batch = tf.train.batch([train_data, train_labels], enqueue_many=true , batch_size=100, allow_smaller_final_batch=true)  tf.session() sess:     # tensorboard --logdir=./logs/planesnet2_log     merged_summary = tf.summary.merge_all()     writer = tf.summary.filewriter('./logs/planesnet2_log')        writer.add_graph(sess.graph)      sess.run(tf.global_variables_initializer())     coord = tf.train.coordinator()     threads = tf.train.start_queue_runners(coord=coord)     total_cost = 0      step in range(20):         x_batch, y_batch = sess.run([train_data_batch, train_labels_batch])         feed_dict = {x: x_batch, y: y_batch}         _, cost_val = sess.run([optimizer, cost], feed_dict = feed_dict)         total_cost += cost_val         print('total_cost: ', total_cost, 'cost_val: ', cost_val)     coord.request_stop()     coord.join(threads)

you use cross entropy loss without sigmoid activation function hypothesis, values not bounded in ]0,1]. log function not defined negative values , somes. add sigmoid , epsilon factor avoid negative or 0 values , should fine.


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