tensorlayer
A problem when using tf.saved_model.save in tensorlayer2.x
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Issue Description
When I using the function "tf.saved_model.save()" to save the model of MLP with TensorLayer2.2, I meet an ValueError .Does anyone know how to solve it or can I replace the function "tf.saved_model.save()" with another one ?
the model of MLP is from here
Reproducible Code
- Which OS are you using ? MacOS
- Please provide a reproducible code of your issue. Without any reproducible code, you will probably not receive any help.
[INSERT CODE HERE]
import pprint
import time
import numpy as np
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import Dense, Dropout, Input
from tensorlayer.models import Model
tl.logging.set_verbosity(tl.logging.DEBUG)
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
def get_model(inputs_shape):
ni = Input(inputs_shape)
nn = Dropout(keep=0.8)(ni)
nn = Dense(n_units=800, act=tf.nn.relu)(nn)
nn = Dropout(keep=0.8)(nn)
nn = Dense(n_units=800, act=tf.nn.relu)(nn)
nn = Dropout(keep=0.8)(nn)
nn = Dense(n_units=10, act=tf.nn.relu)(nn)
M = Model(inputs=ni, outputs=nn, name="mlp")
return M
MLP = get_model([None, 784])
pprint.pprint(MLP.config)
n_epoch = 50
batch_size = 500
print_freq = 5
train_weights = MLP.trainable_weights
optimizer = tf.optimizers.Adam(lr=0.0001)
for epoch in range(n_epoch):
start_time = time.time()
for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
MLP.train()
with tf.GradientTape() as tape:
_logits = MLP(X_batch)
_loss = tl.cost.cross_entropy(_logits, y_batch, name='train_loss')
grad = tape.gradient(_loss, train_weights)
optimizer.apply_gradients(zip(grad, train_weights))
if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
MLP.eval() # disable dropout
print("Epoch {} of {} took {}".format(epoch + 1, n_epoch, time.time() - start_time))
train_loss, train_acc, n_iter = 0, 0, 0
for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=False):
_logits = MLP(X_batch)
train_loss += tl.cost.cross_entropy(_logits, y_batch, name='eval_loss')
train_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
n_iter += 1
print(" train loss: {}".format(train_loss / n_iter))
print(" train acc: {}".format(train_acc / n_iter))
val_loss, val_acc, n_iter = 0, 0, 0
for X_batch, y_batch in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=False):
_logits = MLP(X_batch) # is_train=False, disable dropout
val_loss += tl.cost.cross_entropy(_logits, y_batch, name='eval_loss')
val_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
n_iter += 1
print(" val loss: {}".format(val_loss / n_iter))
print(" val acc: {}".format(val_acc / n_iter))
MLP.eval()
test_loss, test_acc, n_iter = 0, 0, 0
for X_batch, y_batch in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=False):
_logits = MLP(X_batch)
test_loss += tl.cost.cross_entropy(_logits, y_batch, name='test_loss')
test_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
n_iter += 1
print(" test loss: {}".format(test_loss / n_iter))
print(" test acc: {}".format(test_acc / n_iter))
tf.saved_model.save(MLP, './save_model')
[Error] ValueError: Expected a Trackable object for export, got mlp( (_inputlayer_1): Input(shape=[None, 784], name='_inputlayer_1') (dropout_1): Dropout(keep=0.8, name='dropout_1') (dense_1): Dense(n_units=800, relu, in_channels='784', name='dense_1') (dropout_2): Dropout(keep=0.8, name='dropout_2') (dense_2): Dense(n_units=800, relu, in_channels='800', name='dense_2') (dropout_3): Dropout(keep=0.8, name='dropout_3') (dense_3): Dense(n_units=10, relu, in_channels='800', name='dense_3') ).
