I am making a CNN with 6 classes. The 8400 training samples are batched into 84 batches of size 100. I run the model and print out the loss after every batch, the loss is always either 0.0 or some arbitrary number in scientific notation such as 1.74463e+06. Is this normal or am i doing something terrible wrong?
def train_network(x):
pred = convolutional_network(x)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y, logits = pred))
train_op = tf.train.AdamOptimizer(learning_rate=0.01).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) # Initialize all the variables
saver = tf.train.Saver()
print("RUNNING SESSION...")
for epoch in range(num_epochs):
train_batch_x = []
train_batch_y = []
for i in range(0, 84):
train_batch_x = BATCHES_IMAGES[i]
train_batch_y = BATCHES_LABELS[i]
print('Starting feed_dict on batch ', i)
_, loss_value = sess.run([train_op, loss], feed_dict={x: train_batch_x, y: train_batch_y})
print('Finished batch --- loss: ', loss_value)
print('Epoch : ', epoch+1, ' of ', num_epochs, ' - Loss: ', loss_value)
correct = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
acc = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:', acc)
save_path = saver.save(sess, MODEL_PATH)
print("Model saved in file: " , save_path)
```
When printing the output i get something like this:
