def update(self, ytrain):
"""
TODO: construct the feed_dict
"""
sess = get_session()
_, loss = sess.run([self.train_op, self.loss], feed_dict={'Y:0' : ytrain})
return loss
def sample(self, input_data, node, islot=0):
"""
Sample from the model graph. For user provided features generates a
response.
"""
addcolon0 = lambda s : self.main_scope + '/' + s + ':0'
node = self.nodes[node]
sess = get_session()
input_data = {addcolon0(key) : value for key, value in input_data.items()}
if self.batch_size is None:
return sess.run(node._islot_to_itensor[islot], feed_dict=input_data)
else:
num_samples =len(list(input_data.values())[0])
if num_samples % self.batch_size:
raise ValueError("The number of samples ({})is not divisible by "
"self.batch_size({})".format(num_samples,
self.batch_size))
res = np.zeros([num_samples] + node._islot_to_shape[islot][1:])
# print("res.shape", res.shape)
i = 0
for batch_data in self.batch_iterator_from_dataset(input_data,
shuffle=False):
r = sess.run(node._islot_to_itensor[islot],
feed_dict=batch_data)
# print("r.shape", r.shape)
res[i:i+self.batch_size] = r
i += 1
return res
def generate(self, t_init_gen=None, specs=None, nsamps=1000): """ """ Yfake = self._build_fake(t_init_gen, specs, nsamps=nsamps) sess = get_session() sess.run(tf.global_variables_initializer()) return sess.run(Yfake)
def train(self, dataset, num_epochs=100):
"""
Trains the model.
The dataset provided by the client should have keys
train_features, train_response
valid_features, valid_response
test_features, test_response
where # is the number of the corresponding Input node, see model graph.
"""
self._check_dataset_correctness(dataset)
train_dataset, _, _ = self.make_datasets(dataset)
batch_size = self.builder.batch_size
sess = get_session()
sess.run(tf.global_variables_initializer())
for _ in range(num_epochs):
self.bender.update(sess,
tuple(zip(*train_dataset.items())),
batch_size=batch_size)
# cost = np.mean(sess.run([self.cost], feed_dict=train_dataset))
cost = self.reduce_op_from_batches(sess, [self.cost], train_dataset)
print(cost)
sess.close()
def train(self, dataset, num_epochs=100):
"""
Train the RNNClassifier model.
The dataset, provided by the client, should have keys
train_features, train_labels
valid_features, valid_response
test_features, test_response
"""
self._check_dataset_correctness(dataset)
train_dataset, _, _ = self.make_datasets(dataset)
batch_size = self.batch_size
sess = get_session()
sess.run(tf.global_variables_initializer())
for _ in range(num_epochs):
self.trainer.update(sess,
tuple(zip(*train_dataset.items())),
batch_size=batch_size)
cost = self.reduce_op_from_batches(sess, [self.cost],
train_dataset)
print(cost)
def generate(self): """ """ sess = get_session() sess.run(tf.global_variables_initializer()) return sess.run(self.Yfake)
def train(self, ytrain, num_epochs=5):
sess = get_session()
sess.run(tf.global_variables_initializer())
for _ in range(num_epochs):
loss = self.update(ytrain)
print(loss)