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我正在尝试对 3D 黑白 MRI 数据进行二进制分类。由于缺乏黑白数据中固有的通道，我正在使用 2D 卷积。我添加了一个维度来排列维度，本质上，这些数据的深度充当了批处理维度。我正在使用数据的子样本，20 个文件，每个文件 189 x 233 x 197。就像一个快速背景。

根据下面的代码，我有一个 csv 文件，其中包含一堆信息，包括我尝试提取的每个文件的标签数据。

import numpy as np
import glob
import os
import tensorflow as tf
import pandas as pd
import glob

import SimpleITK as sitk

from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from keras.preprocessing.image import ImageDataGenerator


from keras.utils import plot_model
from keras.utils import to_categorical
from keras.utils import np_utils

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

from google.colab import drive
drive.mount('/content/gdrive')

datapath = ('/content/gdrive/My Drive/DirectoryTest/All Data/')
patients = os.listdir(datapath)
labels_df = pd.read_csv('/content/Data_Index.csv', index_col = 0 )

labelset = []

for i in patients:
  label = labels_df.loc[i, 'Group']
  if label is 'AD':
    np.char.replace(label, ['AD'], [0])
  if label is 'CN':
    np.char.replace(label, ['CN'], [1])
  labelset.append(label)

label_encoder = LabelEncoder()
labelset = label_encoder.fit_transform(labelset)

labelset = np_utils.to_categorical(labelset, num_classes= 2)

FullDataSet = []

for i in patients:
  a = sitk.ReadImage(datapath + i)
  b = sitk.GetArrayFromImage(a)
  c = np.reshape(b, (189,233,197, 1))
  FullDataSet.append(c)

training_data, testing_data, training_labels, testing_labels = train_test_split(FullDataSet, labelset, train_size=0.70,test_size=0.30)

dataset_train = tf.data.Dataset.from_tensor_slices((training_data, training_labels))
dataset_test = tf.data.Dataset.from_tensor_slices((testing_data, testing_labels))

CNN_model = tf.keras.Sequential(
  [
      #tf.keras.layers.Input(shape=(189, 233, 197, 1), batch_size=2),
      #tf.keras.layers.Reshape((197, 233, 189, 1)),   
                              
      tf.keras.layers.Conv2D(kernel_size=(7, 7), data_format='channels_last', filters=64, activation='relu',
                             padding='same', strides=( 3, 3), input_shape=( 233, 197, 1)),
      #tf.keras.layers.BatchNormalization(center=True, scale=False),
      tf.keras.layers.MaxPool2D(pool_size=(3, 3), padding='same'),
      tf.keras.layers.Dropout(0.20),
      
      tf.keras.layers.Conv2D(kernel_size=( 7, 7), filters=128, activation='relu', padding='same', strides=( 3, 3)),
      #tf.keras.layers.BatchNormalization(center=True, scale=False),
      tf.keras.layers.MaxPool2D(pool_size=(3, 3), padding='same'),
      tf.keras.layers.Dropout(0.20),      

      tf.keras.layers.Conv2D(kernel_size=( 7, 7), filters=256, activation='relu', padding='same', strides=( 3, 3)),
      #tf.keras.layers.BatchNormalization(center=True, scale=False),
      tf.keras.layers.MaxPool2D(pool_size=(3, 3), padding = 'same'),
      tf.keras.layers.Dropout(0.20), 

      # last activation could be either sigmoid or softmax, need to look into this more. Sig for binary output, Soft for multi output 
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(256, activation='relu'),   
      tf.keras.layers.Dense(64, activation='relu'),
      tf.keras.layers.Dropout(0.20),
      tf.keras.layers.Dense(2, activation='softmax')

  ])
# Compile the model
CNN_model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.00001), loss='binary_crossentropy', metrics=['accuracy'])

# print model layers
CNN_model.summary()

CNN_history = CNN_model.fit(dataset_train, epochs=10, validation_data=dataset_test)

当我去拟合模型时，我收到以下错误：

Epoch 1/10
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-35-a8b210ec2e72> in <module>()
      1 #running of the model
      2 #CNN_history = CNN_model.fit(dataset_train, epochs=100, validation_data =dataset_test, validation_steps=1)
----> 3 CNN_history = CNN_model.fit(dataset_train, epochs=10, validation_data=dataset_test)
      4 
      5 

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)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:749 train_step
        y, y_pred, sample_weight, regularization_losses=self.losses)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:149 __call__
        losses = ag_call(y_true, y_pred)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:253 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    /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/keras/losses.py:1605 binary_crossentropy
        K.binary_crossentropy(y_true, y_pred, from_logits=from_logits), axis=-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/keras/backend.py:4829 binary_crossentropy
        bce = target * math_ops.log(output + epsilon())
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1141 binary_op_wrapper
        raise e
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1125 binary_op_wrapper
        return func(x, y, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1457 _mul_dispatch
        return multiply(x, y, name=name)
    /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/math_ops.py:509 multiply
        return gen_math_ops.mul(x, y, name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py:6176 mul
        "Mul", x=x, y=y, 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: Dimensions must be equal, but are 2 and 189 for '{{node binary_crossentropy/mul}} = Mul[T=DT_FLOAT](ExpandDims, binary_crossentropy/Log)' with input shapes: [2,1], [189,2].

我知道 [189,2] 中的 2 与最终的 softmax 层相关联，但我不知道如何处理该信息，或者从这里去哪里。任何帮助将不胜感激，谢谢！

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1 回答 1

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以下是有关您的代码的一些评论，希望对您有所帮助。

使用Conv3D和MaxPool3D

如果您正在处理 3D 图像，那么您几乎肯定应该使用Conv3D而不是Conv2D，而MaxPool3D不是MaxPool2D。这是一个我刚刚测试过的示例（使用随机数据），它似乎工作正常：

import numpy as np
import tensorflow as tf
from tensorflow import keras

train_size = 20
val_size = 5

X_train = np.random.random([train_size, 189, 233, 197]).astype(np.float32)
X_valid = np.random.random([val_size, 189, 233, 197]).astype(np.float32)
y_train = np.random.randint(2, size=train_size).astype(np.float32)
y_valid = np.random.randint(2, size=val_size).astype(np.float32)

CNN_model = keras.Sequential([
      keras.layers.Reshape([189, 233, 197, 1], input_shape=[189, 233, 197]),
      keras.layers.Conv3D(kernel_size=(7, 7, 7), filters=32, activation='relu',
                          padding='same', strides=(3, 3, 3)),
      #keras.layers.BatchNormalization(),
      keras.layers.MaxPool3D(pool_size=(3, 3, 3), padding='same'),
      keras.layers.Dropout(0.20),
      
      keras.layers.Conv3D(kernel_size=(5, 5, 5), filters=64, activation='relu',
                          padding='same', strides=(3, 3, 3)),
      #keras.layers.BatchNormalization(),
      keras.layers.MaxPool3D(pool_size=(2, 2, 2), padding='same'),
      keras.layers.Dropout(0.20),

      keras.layers.Conv3D(kernel_size=(3, 3, 3), filters=128, activation='relu',
                          padding='same', strides=(1, 1, 1)),
      #keras.layers.BatchNormalization(),
      keras.layers.MaxPool3D(pool_size=(2, 2, 2), padding='same'),
      keras.layers.Dropout(0.20),

      keras.layers.Flatten(),
      keras.layers.Dense(256, activation='relu'),   
      keras.layers.Dense(64, activation='relu'),
      keras.layers.Dropout(0.20),
      keras.layers.Dense(1, activation='sigmoid')
  ])

# Compile the model
CNN_model.compile(optimizer=keras.optimizers.Adam(lr=0.00001),
                  loss='binary_crossentropy',
                  metrics=['accuracy'])

# print model layers
CNN_model.summary()

CNN_history = CNN_model.fit(X_train, y_train, epochs=10,
                            validation_data=[X_valid, y_valid])

不要重塑以置换尺寸

关于这两条注释掉的行：

      #tf.keras.layers.Input(shape=(189, 233, 197, 1), batch_size=2),
      #tf.keras.layers.Reshape((197, 233, 189, 1)),

将 189x233x197x1 图像重新整形为 197x233x189x1 不会像您预期的那样工作。它将完全打乱周围的像素，使任务变得更加困难。这类似于将 2x3 图像重塑为 3x2 图像：

>>> img = np.array([[1,2,3],[4,5,6]])
>>> np.reshape(img, [3, 2])
array([[1, 2],
       [3, 4],
       [5, 6]])

请注意，这与旋转图像不同：像素完全混合在一起。

你想要的是这样使用tf.keras.layers.Permute()：

CNN_model = tf.keras.Sequential([
      tf.keras.layers.Permute((3, 2, 1, 4), input_shape=(189, 233, 197, 1)),
      ...
])

因为这些注释掉的行是错误的，我怀疑下面的行也可能是错误的：

c = np.reshape(b, (189,233,197, 1))

我不知道 的形状b，所以请绝对确保它与此np.reshape()操作兼容。例如，如果它的形状是[189, 233, 197]，那很好。但是，例如，如果它是[197, 233, 189]，那么您将需要在重塑之前置换尺寸：

b_permuted = np.transpose(b, [2, 1, 0]) # permute dims
c = np.reshape(b_permuted, [189, 233, 197, 1]) # then add the channels dim

该np.transpose()功能类似于 using Permute()，除了维度是 0-indexed 而不是 1-indexed。

它可能更复杂。例如，如果将 3D 图像存储为并排包含较小 2D 切片的大型 2D 图像，则 的形状b可能类似于[189*197, 233]. 在这种情况下，您需要执行以下操作：

b_reshaped = np.reshape(b, [189, 197, 233, 1])
c = np.transpose(b_reshaped, [0, 2, 1, 3])

我希望这些例子足够清楚。

使用tf.keras，而不是keras

Keras API 有多种实现。一个是keras包，它是“多后端”Keras（使用 安装pip install keras）。另一个是tf.kerasTensorFlow 附带的。您的程序似乎同时使用两者。你应该绝对避免这种情况，它会导致奇怪的问题。

from keras.utils import plot_model # this is multibackend Keras
...
CNN_model = tf.keras.Sequential(...) # this is tf.keras

我强烈建议您卸载多后端 keras，以避免此类错误：pip uninstall keras. 然后通过添加前缀来修复导入tensorflow.，例如：

from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical # note: not from np_utils
...

不要to_categorical()用于二分类

对于二元分类，标签应该只是一个包含 0 和 1 的一维数组，例如np.array([1., 0., 0., 1., 1.]). 代码可以非常简化：

labelset = []

for i in patients:
  label = labels_df.loc[i, 'Group']
  if label == 'AD':  # use `==` instead of `is` to compare strings
    labelset.append(0.)
  elif label == 'CN':
    labelset.append(1.)
  else:
      raise "Oops, unknown label" # I recommend testing possible failure cases

labelset = np.array(labelset)

重要的是，对于二元分类，您应该在输出层使用单个神经元，并且还使用"sigmoid"激活函数（不是"softmax"，用于多类分类）：

CNN_model = tf.keras.Sequential([
      ...
      tf.keras.layers.Dense(1, activation='sigmoid')
])

小评论

  • 您不需要在调用时同时指定 thetrain_size和 the 。test_sizetrain_test_split()

祝你好运！

于 2020-08-13T07:07:32.270 回答