我为Keras中的深度度量学习定义了自定义损失函数，尽管问题很简单，但在Colab中使用GPU加速器的性能却很差。我该怎么做才能加快训练速度？
示例代码：该代码编写为可以在Colab / Jupyter中运行，但是我想它也可以作为脚本运行（我没有尝试过，但是我看不出任何不起作用的明显原因）。
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Flatten, Dense
from tensorflow.keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = (X_train / 255.0).astype('float32')
X_test = (X_test / 255.0).astype('float32')
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)
tf.keras.backend.clear_session()
embedding_size = 3
model = keras.Sequential()
model.add(Flatten(input_shape = input_shape))
model.add(Dense(128, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(embedding_size))
model.summary()
margin = 0.2
def contrastive_loss(y_true, embeddings):
loss = 0.0
b = embeddings.shape[0]
for i in range(0,b):
yi = y_true[i]
xi = embeddings[i]
for j in range(i+1,b):
yj = y_true[j]
xj = embeddings[j]
yij = tf.minimum(1.0, tf.abs(tf.cast(yi-yj, dtype = tf.float32)))
distance = tf.norm(xi-xj)
loss = loss + (1-yij)*distance**2 + yij*tf.maximum(0.0, margin-distance)**2
loss = 0.5 * loss
return loss
model.compile(keras.optimizers.Adam(learning_rate=1e-4), loss=contrastive_loss)
history = model.fit(X_train, y_train, batch_size=32, epochs=10, shuffle=True)
print(X_test.shape)
emb = model.predict(X_test)
print(emb.shape, "emb")
print(y_test.shape, "y_test")
plt.scatter(emb[:,0], emb[:,1], c=y_test, cmap='tab10')
在带有GPU加速器的Colab上，培训非常慢。关于如何加快速度的任何提示？现在，我获得了> 200 ms / step，即每个时期≈380 s。
编辑：最初，我编写了实际代码的简化版本，但是，根据一些评论，加快代码速度的方法可能取决于我在损失函数中实际计算的内容，因此现在我将实际代码放入。抱歉，如果这样会使代码运行缓慢...
EDIT2：我试图在Jeff的答案中实施解决方案，但出现错误，原因是我不明白。这是新的代码：
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Flatten, Dense
from tensorflow.keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = (X_train / 255.0).astype('float32')
X_test = (X_test / 255.0).astype('float32')
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)
tf.keras.backend.clear_session()
embedding_size = 3
model = keras.Sequential()
model.add(Flatten(input_shape = input_shape))
model.add(Dense(128, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(embedding_size))
model.summary()
margin = 0.2
def contrastive_loss(y_true, embeddings):
# it's easier to work with a flattened array
flat_y_true = tf.reshape(y_true, (-1))
# matrix of absolute differences, clipped to 1 (if yi!=yj then yij =1)
yijs = abs(np.subtract.outer(flat_y_true, flat_y_true)).clip(max=1)
# we only need the upper triangular part of the matrix
yijs = yijs[np.triu_indices_from(yijs)]
# first compute row differences of the embeddings matrix, the compute norms for
# each row with axis=2
distances = np.linalg.norm(embeddings[:,None] - embeddings[None], axis=2)
# we only need the upper triangular part, again
distances = distances[np.triu_indices_from(distances)]
loss = ((1-yijs)*(distances**2) + (yijs*((margin-distances).clip(min=0)**2))).sum()*0.5
return loss
model.compile(keras.optimizers.Adam(learning_rate=1e-4), loss=contrastive_loss)
history = model.fit(X_train, y_train, batch_size=32, epochs=10, shuffle=True)
我收到以下错误：
Epoch 1/10
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-1ae713ccf38c> in <module>()
4
5 # When setting batch size, remember we are *quadratically* expanding it in our loss.
----> 6 history = model.fit(X_train, y_train, batch_size=32, epochs=10, shuffle=True)
10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
<ipython-input-4-6c3b8535eb43>:5 contrastive_loss *
flat_y_true = tf.reshape(y_true, (-1))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper **
return target(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py:195 reshape
result = gen_array_ops.reshape(tensor, shape, name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py:8234 reshape
"Reshape", tensor=tensor, shape=shape, name=name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:744 _apply_op_helper
attrs=attr_protos, op_def=op_def)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:593 _create_op_internal
compute_device)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:3485 _create_op_internal
op_def=op_def)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1975 __init__
control_input_ops, op_def)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1815 _create_c_op
raise ValueError(str(e))
ValueError: Shape must be rank 1 but is rank 0 for '{{node contrastive_loss/Reshape}} = Reshape[T=DT_UINT8, Tshape=DT_INT32](ExpandDims, contrastive_loss/Reshape/shape)' with input shapes: [32,1], [].
如果这不是您想要的，我们将亲自实施。
由于损失函数中的python for循环可能是问题所在，因此我们将重点放在此处。
编辑：我想我需要多睡一会，因为我可能发誓我以前numpy可以在自定义tensorflow损失函数中使用函数。将所有操作切换为tensorflow操作，因为实际上并非如此。
编辑完整功能：
def contrastive_loss(y_true, embeddings):
yijs = tf.cast(tf.reshape((y_true[:,None]!=y_true),(y_true.get_shape()[0],y_true.get_shape()[0])),tf.dtypes.float32)
distances = tf.norm(tf.cast(embeddings[:,None]-embeddings,tf.dtypes.float32),axis=2)
dist_len = distances.get_shape()[0]
return tf.math.reduce_sum((1-yijs)*distances**2 + yijs*(tf.maximum(0.2-distances,tf.zeros((dist_len,dist_len)))**2))*0.25
编辑的逐行演练：
def contrastive_loss(y_true, embeddings):
y_true_len = y_true.get_shape()[0] #shape (None,1)
yijs = tf.cast(
tf.reshape(
(
#Max clipping abs diff of every value and all other values
#to 1 is essentially checking whether
#the other values are equal to itself or not
#this would be a much faster method of doing so
y_true[:,None]!=y_true
),
#resize to square matrix of shape (batch_size,batch_size)
(y_true_len,y_true_len)
),
tf.dtypes.float32
)
distances = tf.norm(
tf.cast(
#find difference between each row and all other rows
embeddings[:,None]-embeddings,
tf.dtypes.float32
),
#normalize every row to find
#magnitude of every row vector
axis=2
)
#get len to setup equivalent square matrix tensor of 0's later
dist_len = distances.get_shape()[0]
#Sums all values in tensor
loss = tf.math.reduce_sum(
(1-yijs)*(distances**2)
+ yijs*(
#clip all values to be above 0
tf.maximum(
0.2-distances,
#declare tensor of same dimension as 0.2-distances
#for element wise comparison so tf.maximum can do its thing
tf.zeros((dist_len,dist_len))
)**2
)
)*0.25 #since we didn't drop lower triangle, need to multiply by 0.25 instead of 0.5
return loss
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我来说两句