有人能解释一下TensorFlow的工作原理吗？我试图建立一个简单的回归，如下所示：
编辑：我正在更新我的问题，这是我的完整代码，现在这个问题出现在梯度计算中，它返回零。我已经检查了非零的损失值。
import tensorflow as tf
tfe = tf.contrib.eager
tf.enable_eager_execution()
import numpy as np
def make_model():
net = tf.keras.Sequential()
net.add(tf.keras.layers.Dense(4, activation='relu'))
net.add(tf.keras.layers.Dense(1))
return net
def compute_loss(pred, actual):
return tf.reduce_mean(tf.square(tf.subtract(pred, actual)))
def compute_gradient(model, pred, actual):
"""compute gradients with given noise and input"""
with tf.GradientTape() as tape:
loss = compute_loss(pred, actual)
grads = tape.gradient(loss, model.variables)
return grads, loss
def apply_gradients(optimizer, grads, model_vars):
optimizer.apply_gradients(zip(grads, model_vars))
model = make_model()
optimizer = tf.train.AdamOptimizer(1e-4)
x = np.linspace(0,1,1000)
y = x+np.random.normal(0,0.3,1000)
y = y.astype('float32')
train_dataset = tf.data.Dataset.from_tensor_slices((y.reshape(-1,1)))
epochs = 2# 10
batch_size = 25
itr = y.shape[0] // batch_size
for epoch in range(epochs):
for data in tf.contrib.eager.Iterator(train_dataset.batch(25)):
preds = model(data)
grads, loss = compute_gradient(model, preds, data)
print(grads)
apply_gradients(optimizer, grads, model.variables)
# with tf.GradientTape() as tape:
# loss = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(preds, data))))
# grads = tape.gradient(loss, model.variables)
# print(grads)
# optimizer.apply_gradients(zip(grads, model.variables),global_step=None)
eager-mode错误如下：
----------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-a589b9123c80> in <module>
35 grads, loss = compute_gradient(model, preds, data)
36 print(grads)
---> 37 apply_gradients(optimizer, grads, model.variables)
38 # with tf.GradientTape() as tape:
39 # loss = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(preds, data))))
<ipython-input-3-a589b9123c80> in apply_gradients(optimizer, grads, model_vars)
17
18 def apply_gradients(optimizer, grads, model_vars):
---> 19 optimizer.apply_gradients(zip(grads, model_vars))
20
21 model = make_model()
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py in apply_gradients(self, grads_and_vars, global_step, name)
589 if not var_list:
590 raise ValueError("No gradients provided for any variable: %s." %
--> 591 ([str(v) for _, v, _ in converted_grads_and_vars],))
592 with ops.init_scope():
593 self._create_slots(var_list)
ValueError: No gradients provided for any variable:
最佳答案
第1部分：问题实际上是输入的数据类型。默认情况下，keras模型需要float32，但传递的是float64。您可以更改模型的数据类型，也可以将输入更改为float32。
要更改模型：
def make_model():
net = tf.keras.Sequential()
net.add(tf.keras.layers.Dense(4, activation='relu', dtype='float32'))
net.add(tf.keras.layers.Dense(4, activation='relu'))
net.add(tf.keras.layers.Dense(1))
return net
要更改输入：
y = y.astype('float32')第2部分：需要调用在tf.gradienttape（）下计算模型的函数（即
model(data)）。例如，可以用以下方法替换compute_loss方法：def compute_loss(model, x, y):
pred = model(x)
return tf.reduce_mean(tf.square(tf.subtract(pred, y)))
关于python - InvalidArgumentError:无法计算MatMul，因为输入＃0(从零开始)应为浮点张量，但为双张量[Op:MatMul]，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/54255431/