def reset_val_batches(self):
self.val_batches = int(
np.floor(self.val_input_data.shape[0] / self.batch_size))
if self.val_batches > 0:
self.val_input_batches = self.val_input_data
self.val_output_batches = self.val_output_data
self.val_input_batches = self.val_input_batches[:self.batch_size *
self.val_batches]
self.val_output_batches = self.val_output_batches[:self.
batch_size *
self.val_batches]
else:
self.val_input_batches = np.zeros(
(self.batch_size, self.val_input_data.shape))
self.val_output_batches = np.zeros(
(self.batch_size, self.val_output_data.shape))
self.val_input_batches = data.reshape_1D_input(
self.val_input_batches)
self.val_input_batches[:self.val_input_data.
shape[0]] = self.val_input_data
self.val_output_batches[:self.val_output_data.
shape[0]] = self.val_output_data
if self.frame_work == 'Tensorflow':
self.val_input_batches, self.val_output_batches = data.convert_to_tensorflow_minbatch(
self.val_input_batches, self.val_output_batches,
self.batch_size)
def reset_train_batches(self, batch_size=None, num_batches=None):
''' This function resets the training batches
Example: Training data set = 1000 samples,
Batch size = 300
Framework: Keras
- The output is an array of as many training samples that fit within the batch size.
Train_input_batches.shape = [900, data_size]
Framework: Tensorflow
- The Tensorflow implementation requires each mini-batch to be explicitly set.
Train_input_batches.shape = (# batches, ) - In each batch is a numpy array of size (batch_size, data_size)
:param batch_size:
:param num_batches:
:return:
'''
# Update batch size if passed
if batch_size is not None:
self.batch_size = batch_size
# Calc number of batches
if num_batches is not None:
self.num_train_batches = int(num_batches)
else:
self.num_train_batches = int(
np.floor(self.train_input_data.shape[0] / self.batch_size))
# Copy all training data
self.train_input_batches = self.train_input_data
self.train_output_batches = self.train_output_data
# Shuffle Training data
self.train_input_batches, self.train_output_batches = data.shuffle_input_output(
self.train_input_batches, self.train_output_batches)
## Restrict the amount of training Data a number that fits in the number of batches
self.train_input_batches = self.train_input_batches[:self.batch_size *
self.
num_train_batches]
self.train_output_batches = self.train_output_batches[:self.
batch_size *
self.
num_train_batches]
if self.frame_work == 'Keras':
return
if self.frame_work == 'Tensorflow':
self.train_input_batches, self.train_output_batches = data.convert_to_tensorflow_minbatch(
self.train_input_batches, self.train_output_batches,
self.batch_size)
