将TensorFlow 1.x版本重写为2.x版本

Question

我需要将Tensorflow 1.x代码重写为2.x版本。因此，我将注释代码重写如下(不同的激活和初始化程序由我自己修改(：

def model(X, nact):
# h = conv(tf.cast(X, tf.float32), nf=32, rf=8, stride=1, init_scale=np.sqrt(2))
h = tf.keras.layers.Conv2D(filters=32,
kernel_size=8,
activation='relu',
kernel_initializer=orthogonal(np.sqrt(2)))(X)
# h2 = conv(h, nf=64, rf=4, stride=1, init_scale=np.sqrt(2))
h2 = tf.keras.layers.Conv2D(filters=64,
kernel_size=4,
activation='relu',
kernel_initializer=orthogonal(np.sqrt(2)))(h)
. . .
# pi = fc(h4, nact, act=lambda x: x)
pi = tf.keras.layers.Dense(units=nact,
activation='linear',
kernel_initializer=orthogonal(np.sqrt(2)))(h4)
# vf = fc(h4, 1, act=lambda x: tf.tanh(x))
vf = tf.keras.layers.Dense(units=1,
activation='tanh',
kernel_initializer=orthogonal(np.sqrt(2)))(h4)
# filter out non-valid actions from pi
valid = tf.reduce_max(tf.cast(X, tf.float32), axis=1)
valid_flat = tf.reshape(valid, [-1, nact])
pi_fil = pi + (valid_flat - tf.ones(tf.shape(valid_flat))) * 1e32
return pi_fil, vf[:, 0]

一些方法进一步我有以下：

def build_model(args):
nh = args.max_clause
nw = args.max_var
nc = 2
nact = nc * nw
ob_shape = (None, nh, nw, nc * args.n_stack)
X = tf.placeholder(tf.float32, ob_shape)
Y = tf.placeholder(tf.float32, (None, nact))
Z = tf.placeholder(tf.float32, None)
p, v = model(X, nact)
params = tf.trainable_variables()
with tf.name_scope("loss"):
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y, logits=p))
value_loss = tf.losses.mean_squared_error(labels=Z, predictions=v)
lossL2 = tf.add_n([tf.nn.l2_loss(vv) for vv in params])
loss = cross_entropy + value_loss + args.l2_coeff * lossL2
return X, Y, Z, p, v, params, loss
def self_play(args, status_track):
X, _, _, p, v, params, _ = build_model(args)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
model_dir = status_track.get_model_dir()
sess.run(load(params, os.path.join(args.save_dir, model_dir)))
. . .
def super_train(args, status_track):
X, Y, Z, _, _, params, loss = build_model(args)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(1e-3).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
model_dir = status_track.get_sl_starter()
sess.run(load(params, os.path.join(args.save_dir, model_dir)))
. . .

如何在TensorFlow 2.x中重写这两个函数，即类似Keras的风格？

Answer 1

Tensorflow 2.x兼容的代码片段。

def build_model(args):
nh = args.max_clause
nw = args.max_var
nc = 2
nact = nc * nw
ob_shape = (None, nh, nw, nc * args.n_stack)
X = tf.compat.v1.placeholder(tf.float32, ob_shape)
Y = tf.compat.v1.placeholder(tf.float32, (None, nact))
Z = tf.compat.v1.placeholder(tf.float32, None)
p, v = model(X, nact)
params = tf.compat.v1.trainable_variables()
with tf.name_scope("loss"):
cross_entropy = tf.reduce_mean(tf.compat.v1.nn.softmax_cross_entropy_with_logits_v2(labels=Y, logits=p))
value_loss = tf.keras.metrics.mean_squared_error(labels=Z, predictions=v)
lossL2 = tf.add_n([tf.compat.v1.nn.l2_loss(vv) for vv in params])
loss = cross_entropy + value_loss + args.l2_coeff * lossL2
return X, Y, Z, p, v, params, loss