import tensorflow as tfimport tensorflow.contrib.eager as tfetfe.enable_eager_execution()x = [[2.]]m = tf.matmul(x, x)
print(m)# The 1x1 matrix [[4.]]
a = tf.constant(12)counter = 0while not tf.equal(a, 1): if tf.equal(a % 2, 0): a = a / 2 else: a = 3 * a + 1 print(a)
def square(x): return tf.multiply(x, x)grad = tfe.gradients_function(square)print(square(3.)) # [9.]print(grad(3.)) # [6.]
gradgrad = tfe.gradients_function(lambda x: grad(x)[0])print(gradgrad(3.)) # [2.]
def abs(x): return x if x > 0. else -xgrad = tfe.gradients_function(abs)print(grad(2.0)) # [1.]print(grad(-2.0)) # [-1.]
def log1pexp(x): return tf.log(1 + tf.exp(x))grad_log1pexp = tfe.gradients_function(log1pexp)# The gradient computation works fine at x = 0.print(grad_log1pexp(0.))# [0.5]# However it returns a `nan` at x = 100 due to numerical instability.print(grad_log1pexp(100.))# [nan]
@tfe.custom_gradientdef log1pexp(x): e = tf.exp(x) def grad(dy): return dy * (1 - 1 / (1 + e)) return tf.log(1 + e), gradgrad_log1pexp = tfe.gradients_function(log1pexp)# Gradient at x = 0 works as before.print(grad_log1pexp(0.))# [0.5]# And now gradient computation at x=100 works as well.print(grad_log1pexp(100.))# [1.0]
class MNISTModel(tfe.Network): def __init__(self): super(MNISTModel, self).__init__() self.layer1 = self.track_layer(tf.layers.Dense(units=10)) self.layer2 = self.track_layer(tf.layers.Dense(units=10)) def call(self, input): """Actually runs the model.""" result = self.layer1(input) result = self.layer2(result) return result
# Let's make up a blank input imagemodel = MNISTModel()batch = tf.zeros([1, 1, 784])print(batch.shape)# (1, 1, 784)result = model(batch)print(result)# tf.Tensor([[[ 0. 0., ...., 0.]]], shape=(1, 1, 10), dtype=float32)
def loss_function(model, x, y): y_ = model(x) return tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)for (x, y) in tfe.Iterator(dataset): grads = tfe.implicit_gradients(loss_function)(model, x, y) optimizer.apply_gradients(grads)
with tf.device("/gpu:0"): for (x, y) in tfe.Iterator(dataset): optimizer.minimize(lambda: loss_function(model, x, y))
No comments :
Post a Comment