我提出了一个示例，其中 tf.keras 模型无法从非常简单的数据中学习。我正在使用 tensorflow-gpu==2.0.0、keras==2.3.0 和 Python 3.7。在文章的最后，我给出了重现我观察到的问题的 Python 代码。
- 数据
样本是形状为 (6, 16, 16, 16, 3) 的 Numpy 数组。为了让事情变得非常简单，我只考虑充满 1 和 0 的数组。带有 1 的数组被赋予标签 1，带有 0 的数组被赋予标签 0。我可以使用以下代码生成一些样本(在下面，n_samples = 240):
def generate_fake_data():
for j in range(1, 240 + 1):
if j < 120:
yield np.ones((6, 16, 16, 16, 3)), np.array([0., 1.])
else:
yield np.zeros((6, 16, 16, 16, 3)), np.array([1., 0.])
为了在 tf.keras 模型中输入这些数据，我使用下面的代码创建了 tf.data.Dataset 的实例。这实际上会创建 BATCH_SIZE = 12 样本的混洗批处理。
def make_tfdataset(for_training=True):
dataset = tf.data.Dataset.from_generator(generator=lambda: generate_fake_data(),
output_types=(tf.float32,
tf.float32),
output_shapes=(tf.TensorShape([6, 16, 16, 16, 3]),
tf.TensorShape([2])))
dataset = dataset.repeat()
if for_training:
dataset = dataset.shuffle(buffer_size=1000)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
我提出以下模型来对我的样本进行分类:
def create_model(in_shape=(6, 16, 16, 16, 3)):
input_layer = Input(shape=in_shape)
reshaped_input = Lambda(lambda x: K.reshape(x, (-1, *in_shape[1:])))(input_layer)
conv3d_layer = Conv3D(filters=64, kernel_size=8, strides=(2, 2, 2), padding='same')(reshaped_input)
relu_layer_1 = ReLU()(conv3d_layer)
pooling_layer = GlobalAveragePooling3D()(relu_layer_1)
reshape_layer_1 = Lambda(lambda x: K.reshape(x, (-1, in_shape[0] * 64)))(pooling_layer)
expand_dims_layer = Lambda(lambda x: K.expand_dims(x, 1))(reshape_layer_1)
conv1d_layer = Conv1D(filters=1, kernel_size=1)(expand_dims_layer)
relu_layer_2 = ReLU()(conv1d_layer)
reshape_layer_2 = Lambda(lambda x: K.squeeze(x, 1))(relu_layer_2)
out = Dense(units=2, activation='softmax')(reshape_layer_2)
return Model(inputs=[input_layer], outputs=[out])
该模型使用 Adam(使用默认参数)和 binary_crossentropy 损失进行优化:
clf_model = create_model()
clf_model.compile(optimizer=Adam(),
loss='categorical_crossentropy',
metrics=['accuracy', 'categorical_crossentropy'])
clf_model.summary() 的输出是:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 6, 16, 16, 16, 3) 0
_________________________________________________________________
lambda (Lambda) (None, 16, 16, 16, 3) 0
_________________________________________________________________
conv3d (Conv3D) (None, 8, 8, 8, 64) 98368
_________________________________________________________________
re_lu (ReLU) (None, 8, 8, 8, 64) 0
_________________________________________________________________
global_average_pooling3d (Gl (None, 64) 0
_________________________________________________________________
lambda_1 (Lambda) (None, 384) 0
_________________________________________________________________
lambda_2 (Lambda) (None, 1, 384) 0
_________________________________________________________________
conv1d (Conv1D) (None, 1, 1) 385
_________________________________________________________________
re_lu_1 (ReLU) (None, 1, 1) 0
_________________________________________________________________
lambda_3 (Lambda) (None, 1) 0
_________________________________________________________________
dense (Dense) (None, 2) 4
=================================================================
Total params: 98,757
Trainable params: 98,757
Non-trainable params: 0
模型训练了 500 个时期，如下所示:
train_ds = make_tfdataset(for_training=True)
history = clf_model.fit(train_ds,
epochs=500,
steps_per_epoch=ceil(240 / BATCH_SIZE),
verbose=1)
During the 500 epochs, the model loss stays around 0.69 and never goes below 0.69. This is also true if I set the learning rate to
1e-2instead of1e-3. The data is very simple (just 0s and 1s). Naively, I would expect the model to have a better accuracy than just 0.6. In fact, I would expect it to reach 100% accuracy quickly. What I am doing wrong?
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
from math import ceil
from tensorflow.keras.layers import Input, Dense, Lambda, Conv1D, GlobalAveragePooling3D, Conv3D, ReLU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
BATCH_SIZE = 12
def generate_fake_data():
for j in range(1, 240 + 1):
if j < 120:
yield np.ones((6, 16, 16, 16, 3)), np.array([0., 1.])
else:
yield np.zeros((6, 16, 16, 16, 3)), np.array([1., 0.])
def make_tfdataset(for_training=True):
dataset = tf.data.Dataset.from_generator(generator=lambda: generate_fake_data(),
output_types=(tf.float32,
tf.float32),
output_shapes=(tf.TensorShape([6, 16, 16, 16, 3]),
tf.TensorShape([2])))
dataset = dataset.repeat()
if for_training:
dataset = dataset.shuffle(buffer_size=1000)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
def create_model(in_shape=(6, 16, 16, 16, 3)):
input_layer = Input(shape=in_shape)
reshaped_input = Lambda(lambda x: K.reshape(x, (-1, *in_shape[1:])))(input_layer)
conv3d_layer = Conv3D(filters=64, kernel_size=8, strides=(2, 2, 2), padding='same')(reshaped_input)
relu_layer_1 = ReLU()(conv3d_layer)
pooling_layer = GlobalAveragePooling3D()(relu_layer_1)
reshape_layer_1 = Lambda(lambda x: K.reshape(x, (-1, in_shape[0] * 64)))(pooling_layer)
expand_dims_layer = Lambda(lambda x: K.expand_dims(x, 1))(reshape_layer_1)
conv1d_layer = Conv1D(filters=1, kernel_size=1)(expand_dims_layer)
relu_layer_2 = ReLU()(conv1d_layer)
reshape_layer_2 = Lambda(lambda x: K.squeeze(x, 1))(relu_layer_2)
out = Dense(units=2, activation='softmax')(reshape_layer_2)
return Model(inputs=[input_layer], outputs=[out])
train_ds = make_tfdataset(for_training=True)
clf_model = create_model(in_shape=(6, 16, 16, 16, 3))
clf_model.summary()
clf_model.compile(optimizer=Adam(lr=1e-3),
loss='categorical_crossentropy',
metrics=['accuracy', 'categorical_crossentropy'])
history = clf_model.fit(train_ds,
epochs=500,
steps_per_epoch=ceil(240 / BATCH_SIZE),
verbose=1)
最佳答案
您的代码有一个关键问题:维度改组。您应该永远接触的一个维度是批处理维度 - 因为根据定义，它保存数据的独立样本。在第一次 reshape 中，您将特征尺寸与批量尺寸混合:
Tensor("input_1:0", shape=(12, 6, 16, 16, 16, 3), dtype=float32)
Tensor("lambda/Reshape:0", shape=(72, 16, 16, 16, 3), dtype=float32)
这就像喂 72 个独立的形状样本 (16,16,16,3) 。其他层也遇到类似的问题。
- 不要 reshape 每一步(您应该使用
Reshape)，而是塑造现有的卷积层和池化层，让一切直接顺利进行。 - 除了输入和输出层之外，最好为每个层命名简短的内容 - 不会丢失清晰度，因为每行都由层名称明确定义
-
GlobalAveragePooling旨在成为最后层，因为它折叠功能尺寸 - 在您的情况下，如下所示:(12,16,16,16,3) --> (12,3);之后的转换没有多大作用 - 根据上面的内容，我替换了
Conv1D与Conv3D - 除非您使用可变批量大小，否则始终选择
batch_shape=与shape=，因为您可以完整检查图层尺寸(非常有用) - 你的真实
batch_size这里是 6，从您的评论回复中推导出来 -
kernel_size=1和(特别是)filters=1是一个非常弱的卷积，我相应地替换了它 - 如果您愿意，您可以恢复 - 如果您的预期应用程序中只有 2 个类，我建议使用
Dense(1, 'sigmoid')与binary_crossentropy损失
最后一点:除了维度改组建议之外，您可以将上述所有内容都扔掉，但仍然可以获得完美的训练集性能；这是问题的根源。
def create_model(batch_size, input_shape):
ipt = Input(batch_shape=(batch_size, *input_shape))
x = Conv3D(filters=64, kernel_size=8, strides=(2, 2, 2),
activation='relu', padding='same')(ipt)
x = Conv3D(filters=8, kernel_size=4, strides=(2, 2, 2),
activation='relu', padding='same')(x)
x = GlobalAveragePooling3D()(x)
out = Dense(units=2, activation='softmax')(x)
return Model(inputs=ipt, outputs=out)
BATCH_SIZE = 6
INPUT_SHAPE = (16, 16, 16, 3)
BATCH_SHAPE = (BATCH_SIZE, *INPUT_SHAPE)
def generate_fake_data():
for j in range(1, 240 + 1):
if j < 120:
yield np.ones(INPUT_SHAPE), np.array([0., 1.])
else:
yield np.zeros(INPUT_SHAPE), np.array([1., 0.])
def make_tfdataset(for_training=True):
dataset = tf.data.Dataset.from_generator(generator=lambda: generate_fake_data(),
output_types=(tf.float32,
tf.float32),
output_shapes=(tf.TensorShape(INPUT_SHAPE),
tf.TensorShape([2])))
dataset = dataset.repeat()
if for_training:
dataset = dataset.shuffle(buffer_size=1000)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
结果:
Epoch 28/500
40/40 [==============================] - 0s 3ms/step - loss: 0.0808 - acc: 1.0000
关于python - Keras 模型未能减少损失，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/58237726/