def test():
model_to_be_restored = MLP()
checkpoint = tf.train.Checkpoint(
myAwesomeModel=model_to_be_restored
) # 实例化Checkpoint，设置恢复对象为新建立的模型model_to_be_restored
checkpoint.restore(tf.train.latest_checkpoint('./save')) # 从文件恢复模型参数
num_eval_samples = np.shape(data_loader.eval_labels)[0]
y_pred = model_to_be_restored.predict(tf.constant(
data_loader.eval_data)).numpy()
print("test accuracy: %f" %
(sum(y_pred == data_loader.eval_labels) / num_eval_samples))
def test():
model_to_be_restored = MLP()
checkpoint = tf.train.Checkpoint(
myAwesomeModel=model_to_be_restored
) # instantiate a Checkpoint, set newly initialized model `model_to_be_restored` to be the object to be restored
checkpoint.restore(tf.train.latest_checkpoint(
'./save')) # restore parameters of model from file
num_eval_samples = np.shape(data_loader.eval_labels)[0]
y_pred = model_to_be_restored.predict(tf.constant(
data_loader.eval_data)).numpy()
print("test accuracy: %f" %
(sum(y_pred == data_loader.eval_labels) / num_eval_samples))
def train():
model = MLP()
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
checkpoint = tf.train.Checkpoint(
myAwesomeModel=model) # 实例化Checkpoint，设置保存对象为model
for batch_index in range(num_batches):
X, y = data_loader.get_batch(batch_size)
with tf.GradientTape() as tape:
y_logit_pred = model(tf.convert_to_tensor(X))
loss = tf.losses.sparse_softmax_cross_entropy(labels=y,
logits=y_logit_pred)
print("batch %d: loss %f" % (batch_index, loss.numpy()))
grads = tape.gradient(loss, model.variables)
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))
if (batch_index + 1) % 100 == 0: # 每隔100个Batch保存一次
checkpoint.save('./save/model.ckpt') # 保存模型参数到文件
def train():
model = MLP()
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
checkpoint = tf.train.Checkpoint(
myAwesomeModel=model
) # instantiate a Checkpoint, set `model` as object to be saved
for batch_index in range(num_batches):
X, y = data_loader.get_batch(batch_size)
with tf.GradientTape() as tape:
y_logit_pred = model(tf.convert_to_tensor(X))
loss = tf.losses.sparse_softmax_cross_entropy(labels=y,
logits=y_logit_pred)
print("batch %d: loss %f" % (batch_index, loss.numpy()))
grads = tape.gradient(loss, model.variables)
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))
if (batch_index + 1) % 100 == 0: # save every 100 batches
checkpoint.save('./save/model.ckpt') # save model to .ckpt file
def __init__(self):
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
self.train_data = mnist.train.images # np.array [55000, 784].
self.train_labels = np.asarray(
mnist.train.labels, dtype=np.int32) # np.array [55000] of int32.
self.eval_data = mnist.test.images # np.array [10000, 784].
self.eval_labels = np.asarray(
mnist.test.labels, dtype=np.int32) # np.array [10000] of int32.
def get_batch(self, batch_size):
index = np.random.randint(0, np.shape(self.train_data)[0], batch_size)
return self.train_data[index, :], self.train_labels[index]
if model_type == 'MLP':
model = MLP()
else:
model = CNN()
data_loader = DataLoader()
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
for batch_index in range(num_batches):
X, y = data_loader.get_batch(batch_size)
with tf.GradientTape() as tape:
y_logit_pred = model(tf.convert_to_tensor(X))
loss = tf.losses.sparse_softmax_cross_entropy(labels=y,
logits=y_logit_pred)
print("batch %d: loss %f" % (batch_index, loss.numpy()))
grads = tape.gradient(loss, model.variables)
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))
num_eval_samples = np.shape(data_loader.eval_labels)[0]
import tensorflow as tf
import numpy as np
from en.model.mlp.mlp import MLP
from en.model.mlp.utils import DataLoader
tf.enable_eager_execution()
num_batches = 10000
batch_size = 50
learning_rate = 0.001
model = MLP()
data_loader = DataLoader()
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
summary_writer = tf.contrib.summary.create_file_writer(
'./tensorboard') # 实例化记录器
with summary_writer.as_default(), tf.contrib.summary.always_record_summaries():
for batch_index in range(num_batches):
X, y = data_loader.get_batch(batch_size)
with tf.GradientTape() as tape:
y_logit_pred = model(tf.convert_to_tensor(X))
loss = tf.losses.sparse_softmax_cross_entropy(labels=y,
logits=y_logit_pred)
print("batch %d: loss %f" % (batch_index, loss.numpy()))
tf.contrib.summary.scalar("loss", loss,
step=batch_index) # 记录当前loss
grads = tape.gradient(loss, model.variables)
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))