def create_model(params, is_train):
with tf.name_scope('model'):
if is_train:
inputs = layers.Input((None, ), dtype=tf.int64, name='inputs')
targets = layers.Input((None, ), dtype=tf.int64, name='targets')
internal_model = Transformer(params, name='transformer')
logits = internal_model([inputs, targets], training=is_train)
vocab_size = params['vocab_size']
label_smoothing = params['label_smoothing']
if params['enable_metrics_in_training']:
logits = metrics.MetricLayer(vocab_size)([logits, targets])
logits = layers.Lambda(lambda x: x,
name='logits',
dtype=tf.float32)(logits)
model = Model([inputs, targets], logits)
# TODO: Can we do this loss in float16 instead of float32?
loss = metrics.transformer_loss(logits, targets, label_smoothing,
vocab_size)
model.add_loss(loss)
return model
else:
inputs = layers.Input((None, ), dtype=tf.int64, name='inputs')
internal_model = Transformer(params, name='transformer')
ret = internal_model([inputs], training=is_train)
outputs, scores = ret['outputs'], ret['scores']
return Model(inputs, [outputs, scores])
def generator_loss(fake_output, generated_output, targets):
discriminator_loss = cross_entropy(tf.ones_like(fake_output),
fake_output)
logits = metrics.MetricLayer(
self.vocab_size)([generated_output, targets])
logits, generator_loss = metrics.LossLayer(self.vocab_size,
0.1)([logits, targets])
return discriminator_loss + generator_loss, generator_loss
def test_basic_simpleSeq2Seq(self):
trace_path = "checkpoints_tl/logging/loss"
vocabulary_size = 64
emb_dim = 32
model_ = Seq2seqLuongAttention(hidden_size=128,
embedding_layer=tl.layers.Embedding(
vocabulary_size=vocabulary_size,
embedding_size=emb_dim),
cell=tf.keras.layers.GRUCell,
method="dot")
# print(", ".join(x for x in [t.name for t in model_.trainable_weights]))
self.vocab_size = 64
optimizer = tf.optimizers.Adam(learning_rate=0.01)
for epoch in range(self.num_epochs):
model_.train()
t = time.time()
trainX, trainY = shuffle(self.trainX, self.trainY)
total_loss, n_iter = 0, 0
for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX,
targets=trainY,
batch_size=self.batch_size,
shuffle=False),
total=self.n_step,
desc='Epoch[{}/{}]'.format(
epoch + 1, self.num_epochs),
leave=False):
with tf.GradientTape() as tape:
dec_seq = Y[:, :-1]
targets = Y[:, 1:]
logits = model_(inputs=[X, dec_seq])
logits = metrics.MetricLayer(
self.vocab_size)([logits, targets])
logits, loss = metrics.LossLayer(self.vocab_size,
0.1)([logits, targets])
with tf.io.gfile.GFile(trace_path, "ab+") as trace_file:
trace_file.write(str(loss.numpy()) + '\n')
grad = tape.gradient(loss, model_.all_weights)
optimizer.apply_gradients(zip(grad, model_.all_weights))
total_loss += loss
n_iter += 1
print(time.time() - t)
# tl.files.save_npz(model_.all_weights, name='./model_v4.npz')
model_.eval()
test_sample = trainX[0:2, :]
prediction = model_(inputs=[test_sample], seq_length=10, sos=0)
print("Prediction: >>>>> ", prediction, "\n Target: >>>>> ",
trainY[0:2, 1:], "\n\n")
print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1,
self.num_epochs,
total_loss / n_iter))
def train_step(inputs, targets):
model.train()
with tf.GradientTape() as tape:
#print(inputs)
logits = model(inputs=inputs, targets=targets)
logits = metrics.MetricLayer(params.vocab_size)([logits, targets])
logits, loss = metrics.LossLayer(params.vocab_size,
0.1)([logits, targets])
gradients = tape.gradient(loss, model.all_weights)
optimizer_.apply_gradients(zip(gradients, model.all_weights))
return loss
def train_step(inputs, targets):
with tf.GradientTape() as tape:
#print(inputs)
logits = model(inputs=[inputs, targets], training=True)
logits = metrics.MetricLayer(
params["vocab_size"])([logits, targets])
logits, loss = metrics.LossLayer(params["vocab_size"],
0.1)([logits, targets])
gradients = tape.gradient(loss, model.trainable_weights)
optimizer_.apply_gradients(zip(gradients, model.trainable_weights))
return loss
def train_step(inputs, targets):
model.train()
with tf.GradientTape() as tape:
decoder_inputs = tf.pad(targets,
[[0, 0], [1, 0]])[:, :-1]
logits = model(inputs=[inputs, decoder_inputs])
logits = metrics.MetricLayer(params.vocab_size)([logits, targets])
logits, loss = metrics.LossLayer(params.vocab_size, 0.1)([logits, targets])
gradients = tape.gradient(loss, model.all_weights)
optimizer_.apply_gradients(zip(gradients, model.all_weights))
return loss
def test_basic_simpleSeq2Seq(self):
model_ = Transformer(TINY_PARAMS)
self.vocab_size = TINY_PARAMS.vocab_size
optimizer = tf.optimizers.Adam(learning_rate=0.01)
for epoch in range(self.num_epochs):
model_.train()
t = time.time()
trainX, trainY = shuffle(self.trainX, self.trainY)
total_loss, n_iter = 0, 0
for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX,
targets=trainY,
batch_size=self.batch_size,
shuffle=False),
total=self.n_step,
desc='Epoch[{}/{}]'.format(
epoch + 1, self.num_epochs),
leave=False):
with tf.GradientTape() as tape:
targets = Y
logits = model_(inputs=X, targets=Y)
logits = metrics.MetricLayer(
self.vocab_size)([logits, targets])
logits, loss = metrics.LossLayer(self.vocab_size,
0.1)([logits, targets])
grad = tape.gradient(loss, model_.all_weights)
optimizer.apply_gradients(zip(grad, model_.all_weights))
total_loss += loss
n_iter += 1
print(time.time() - t)
model_.eval()
test_sample = trainX[0:2, :]
model_.eval()
prediction = model_(inputs=test_sample)
print("Prediction: >>>>> ", prediction["outputs"],
"\n Target: >>>>> ", trainY[0:2, :], "\n\n")
print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1,
self.num_epochs,
total_loss / n_iter))
def test_basic_simpleSeq2Seq(self):
trace_path = "checkpoints_tl/logging/loss"
model_ = Transformer(TINY_PARAMS)
# print(", ".join(x for x in [t.name for t in model_.trainable_weights]))
self.vocab_size = TINY_PARAMS.vocab_size
optimizer = tf.optimizers.Adam(learning_rate=0.01)
for epoch in range(self.num_epochs):
model_.train()
t = time.time()
trainX, trainY = shuffle(self.trainX, self.trainY)
total_loss, n_iter = 0, 0
for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=self.batch_size,
shuffle=False), total=self.n_step,
desc='Epoch[{}/{}]'.format(epoch + 1, self.num_epochs), leave=False):
with tf.GradientTape() as tape:
targets = Y
logits = model_(inputs = X, targets = Y)
logits = metrics.MetricLayer(self.vocab_size)([logits, targets])
logits, loss = metrics.LossLayer(self.vocab_size, 0.1)([logits, targets])
with tf.io.gfile.GFile(trace_path, "ab+") as trace_file:
trace_file.write(str(loss.numpy())+'\n')
grad = tape.gradient(loss, model_.all_weights)
optimizer.apply_gradients(zip(grad, model_.all_weights))
total_loss += loss
n_iter += 1
print(time.time()-t)
tl.files.save_npz(model_.all_weights, name='./model_v4.npz')
model_.eval()
test_sample = trainX[0:2, :]
model_.eval()
prediction = model_(inputs = test_sample)
print("Prediction: >>>>> ", prediction["outputs"], "\n Target: >>>>> ", trainY[0:2, :], "\n\n")
print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, self.num_epochs, total_loss / n_iter))
def test_basic_simpleSeq2Seq(self):
model_ = Transformer(params)
self.vocab_size = params["vocab_size"]
# optimizer_ = optimizer.LazyAdam(
# params["learning_rate"],
# params["optimizer_adam_beta1"],
# params["optimizer_adam_beta2"],
# epsilon=params["optimizer_adam_epsilon"])
learning_rate = CustomSchedule(params["hidden_size"])
optimizer_ = tf.optimizers.Adam(learning_rate=0.01)
# optimizer_ = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98,
# epsilon=1e-9)
# optimizer_ = optimizer.LazyAdam(learning_rate, beta_1=0.9, beta_2=0.98,
# epsilon=1e-9)
for epoch in range(self.num_epochs):
trainX, trainY = shuffle(self.trainX, self.trainY)
total_loss, n_iter = 0, 0
for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX,
targets=trainY,
batch_size=self.batch_size,
shuffle=False),
total=self.n_step,
desc='Epoch[{}/{}]'.format(
epoch + 1, self.num_epochs),
leave=False):
with tf.GradientTape() as tape:
targets = Y
output = model_(inputs=[X, Y], training=True)
# print(len(model_.trainable_weights))
# print(model_.trainable_weights)
# exit()
# print(logits.shape, Y.shape)
logits = metrics.MetricLayer(
self.vocab_size)([output, targets])
logits, loss = metrics.LossLayer(self.vocab_size,
0.1)([logits, targets])
# logits = tf.keras.layers.Lambda(lambda x: x, name="logits")(logits)
# print(time.time()-start)
# output = tf.reshape(output, [-1, output.shape[-1]])
# print(", ".join([t.name for t in model_.trainable_weights]))
# layer_normalization_print = [x for x in [t.name for t in model_.trainable_weights] if "feed_forward_network" in x ]
# print(", ".join(x for x in [t.name for t in model_.trainable_weights] if "feed_forward_network" in x ))
# print("number of layers : ", len(model_.trainable_weights))
# exit()
# loss = cross_entropy_seq(logits=output, target_seqs=Y)
grad = tape.gradient(loss, model_.trainable_weights)
# print(grad)
# exit()
optimizer_.apply_gradients(
zip(grad, model_.trainable_weights))
# print(time.time()-start)
total_loss += loss
n_iter += 1
test_sample = trainX[0:2, :]
top_n = 1
for i in range(top_n):
prediction = model_(inputs=[test_sample], training=False)
print("Prediction: >>>>> ", prediction["outputs"],
"\n Target: >>>>> ", trainY[0:2, :], "\n\n")
# printing average loss after every epoch
print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1,
self.num_epochs,
total_loss / n_iter))
