Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- import sys
- ####################
- ### Set the hyperparameters in you myanswers.py file ###
- ####################
- from my_answers import iterations, learning_rate, hidden_nodes, output_nodes
- N_i = train_features.shape[1]
- network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
- losses = {'train':[], 'validation':[]}
- for ii in range(iterations):
- # Go through a random batch of 128 records from the training data set
- batch = np.random.choice(train_features.index, size=128)
- X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
- network.train(X, y)
- # Printing out the training progress
- train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
- val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
- sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
- + "% ... Training loss: " + str(train_loss)[:5] \
- + " ... Validation loss: " + str(val_loss)[:5])
- sys.stdout.flush()
- losses['train'].append(train_loss)
- losses['validation'].append(val_loss)
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement