hop of those help? In simple terms, when you build the TF graph up to the point you are computing the loss in your code, TF will know on which tf.Variable (weights) the loss depends. Then, when you create the node train = tf.train.GradientDescentOptimizer(1.0).minimize(loss), and later run it in a tf.Session, the backpropagation is done for you in the background. To be more specific, the train = tf.train.GradientDescentOptimizer(1.0).minimize(loss) merges the following steps:
# 1. Create a GD optimizer with a learning rate of 1.0
optimizer = tf.train.GradientDescentOptimizer(1.0)
# 2. Compute the gradients for each of the variables (weights) with respect to the loss
gradients, variables = zip(*optimizer.compute_gradients(loss))
# 3. Update the variables (weights) based on the computed gradients
train = optimizer.apply_gradients(zip(gradients, variables))
I hope this helps you . When you create an optimizer (e.g. tf.train.AdagradOptimizer) to train your model, you can pass an explicit var_list=[...] argument to the Optimizer.minimize() method. (If you don't specify this list, it will default to containing all of the variables in tf.trainable_variables().) For example, depending on your model, you might be able to use the names of your variables to define the list of variables to be optimized:
# Assuming all variables to be fine-tuned have a name that starts with
opt_vars = [v for v in tf.trainable_variables() if v.name.startswith("layer17/")]
train_op = optimizer.minimize(loss, var_list=opt_vars)
nan loss when training a deep neural network in tensorflow tutorial
wish of those help The MDNN explained in the paper individually trains several models using random (but bounded) distortions on the data. Once all models are trained, they produce predictions using an ensemble classifier by averaging the output of all the models on different versions of the data. As far as I understand, the columns are not jointly but independently trained. So you must create different models and call fit on each on them. I recommend you start training a single model and once you have a training setting getting good results, replicate it. To generate predictions, you must compute the average of the predicted probabilities from the predict function and take the most probable class.
How to specify the architecture of deep neural network in Tensorflow?
hope this fix your issue I would strongly recommend working through their hands on tutorial, depending on if you have previous ML experience (https://www.tensorflow.org/get_started/mnist/pros) or not (https://www.tensorflow.org/get_started/mnist/beginners). The questions you are asking are answered in there. The question on using predefined architectures or self defined depends on your use case. If you want to do something easy like classifying if there is only a car in the scene or not a more shallow architecture might work better, because it is faster and a more deep one is overkill. However most architectures are similar to the ones already defined in literature.
Low accuracy in Deep Neural Network with Tensorflow
like below fixes the issue The problem was caused by nan in loss function and weights, as described in this question. By introducing a different standard deviation for each weights tensor based on its dimensions (as described in this answer and originally in He et al. ) I was able to train successfully the network.