m = X_.shape[0]
batch_size = 11
steps_per_epoch = m // batch_size
graph = Graph(feed_dict)
sgd = SGD(1e-2)
trainables = [W1, b1, W2, b2]
print("Total number of examples = {}".format(m))
for i in range(epochs):
loss = 0
for j in range(steps_per_epoch):
# Step 1
# Randomly sample a batch of examples
X_batch, y_batch = resample(X_, y_, n_samples=batch_size)
# Reset value of X and y Inputs
X.output = X_batch
y.output = y_batch
graph.compute_gradients()
sgd.update(trainables)
loss += graph.loss()
print("Epoch: {}, Loss: {:.3f}".format(i + 1, loss / steps_per_epoch))
