I've a problem for resuming training after saving my model. The problem is that my loss decrease form 6 to 3 for example. At this time I save the model. When I restore it and continue training, the loss restart from 6. It seems that the restoration doesn't really work. I don't understand because printing the weights, it seems that they are loaded properly. I use an ADAM optimizer. Thanks in advance. Here:
batch_size = self.batch_size
num_classes = self.num_classes
n_hidden = 50 #700
n_layers = 1 #3
truncated_backprop = self.seq_len
dropout = 0.3
learning_rate = 0.001
epochs = 200
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [batch_size, truncated_backprop], name='x')
y = tf.placeholder(tf.int32, [batch_size, truncated_backprop], name='y')
with tf.name_scope('weights'):
W = tf.Variable(np.random.rand(n_hidden, num_classes), dtype=tf.float32)
b = tf.Variable(np.random.rand(1, num_classes), dtype=tf.float32)
inputs_series = tf.split(x, truncated_backprop, 1)
labels_series = tf.unstack(y, axis=1)
with tf.name_scope('LSTM'):
cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, state_is_tuple=True)
cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=dropout)
cell = tf.contrib.rnn.MultiRNNCell([cell] * n_layers)
states_series, current_state = tf.contrib.rnn.static_rnn(cell, inputs_series, \
dtype=tf.float32)
logits_series = [tf.matmul(state, W) + b for state in states_series]
prediction_series = [tf.nn.softmax(logits) for logits in logits_series]
losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels) \
for logits, labels, in zip(logits_series, labels_series)]
total_loss = tf.reduce_mean(losses)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(total_loss)
tf.summary.scalar('total_loss', total_loss)
summary_op = tf.summary.merge_all()
loss_list = []
writer = tf.summary.FileWriter('tf_logs', graph=tf.get_default_graph())
all_saver = tf.train.Saver()
with tf.Session() as sess:
#sess.run(tf.global_variables_initializer())
tf.reset_default_graph()
saver = tf.train.import_meta_graph('./models/tf_models/rnn_model.meta')
saver.restore(sess, './models/tf_models/rnn_model')
for epoch_idx in range(epochs):
xx, yy = next(self.get_batch)
batch_count = len(self.D.chars) // batch_size // truncated_backprop
for batch_idx in range(batch_count):
batchX, batchY = next(self.get_batch)
summ, _total_loss, _train_step, _current_state, _prediction_series = sess.run(\
[summary_op, total_loss, train_step, current_state, prediction_series],
feed_dict = {
x : batchX,
y : batchY
})
loss_list.append(_total_loss)
writer.add_summary(summ, epoch_idx * batch_count + batch_idx)
if batch_idx % 5 == 0:
print('Step', batch_idx, 'Batch_loss', _total_loss)
if batch_idx % 50 == 0:
all_saver.save(sess, 'models/tf_models/rnn_model')
if epoch_idx % 5 == 0:
print('Epoch', epoch_idx, 'Last_loss', loss_list[-1])
My problem was a code error in labels, they were changing between two run. So it works now. Thank you for the help