ValueError в форме потери категорийной_кросентропии - PullRequest
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ValueError в форме потери категорийной_кросентропии

0 голосов
/ 08 февраля 2020

Я строю мультиклассовую модель CNN, но не могу скомпилировать модель из-за ошибки формы потери.

  • И выходной слой, и метки должны иметь правильные формы; метки (m, 1, 3) и конечный плотный слой, содержащий 3 восприятия с активацией softmax
  • loss = 'categoryorical_crossentropy' 
import numpy as np
import pandas as pd
from preprocess import DataLoader

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv3D, Dropout, MaxPooling3D
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras import optimizers

target_width = 160
target_height = 192
target_depth = 192

num_classes = 3
batch_size = 4

data_loader = DataLoader(target_shape=(target_width, target_height, target_depth))
train, test = data_loader.Get_Data_List()

print("Train size: " + str(len(train)))
print("Test size: " + str(len(test)))

def custom_one_hot(labels):
  label_dict = {"stableAD":np.array([0,0,1]),
              "stableMCI":np.array([0,1,0]),
              "stableNL":np.array([1,0,0])}
  encoded_labels = []
  for label in labels:
    encoded_labels.append(label_dict[label].reshape(1,3))
  return np.asarray(encoded_labels)

def additional_data_prep(train, test):
  # Extract data from tuples
  train_labels, train_data = zip(*train)
  test_labels, test_data = zip(*test)
  X_train = np.asarray(train_data)
  X_test = np.asarray(test_data)
  y_train = custom_one_hot(train_labels)
  y_test = custom_one_hot(test_labels)
  return X_train, y_train, X_test, y_test

X, y, X_test, y_test = additional_data_prep(train, test)

X = np.expand_dims(X, axis=-1).reshape((X.shape[0],target_width,target_height,target_depth,1))
X_test = np.expand_dims(X_test, axis=-1).reshape((X_test.shape[0],target_width,target_height,target_depth,1))

model = Sequential()
model.add(Conv3D(24, kernel_size=(13, 11, 11), activation='relu', input_shape=(target_width,target_height,target_depth,1), padding='same', strides=4))
model.add(MaxPooling3D(pool_size=(3, 3, 3), strides=2))
model.add(Dropout(0.1))
model.add(Conv3D(48, kernel_size=(6, 5, 5), activation='relu', padding='same'))
model.add(MaxPooling3D(pool_size=(3, 3, 3), strides=2))
model.add(Dropout(0.1))
model.add(Conv3D(24, kernel_size=(4, 3, 3), activation='relu'))
model.add(MaxPooling3D(pool_size=(3, 3, 3), strides=2))
model.add(Dropout(0.1))
model.add(Conv3D(8, kernel_size=(2, 2, 2), activation='relu'))
model.add(MaxPooling3D(pool_size=(1, 1, 1), strides=2))
model.add(Dropout(0.1))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer=optimizers.Adam(learning_rate=0.0015),
              metrics=['accuracy','categorical_crossentropy'])

model.fit(X, y, batch_size=batch_size, epochs=10, verbose=2, use_multiprocessing=True)  

model.evaluate(X_test, y_test, verbose=2, use_multiprocessing=True)

Результаты в этом сообщении об ошибке: 

Traceback (most recent call last):
  File "train.py", line 70, in <module>
    model.fit(X, y, batch_size=batch_size, epochs=10, verbose=2, use_multiprocessing=True)
  File "/home/554282/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py", line 819, in fit
    use_multiprocessing=use_multiprocessing)
  File "/home/554282/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 235, in fit
    use_multiprocessing=use_multiprocessing)
  File "/home/554282/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 593, in _process_training_inputs
    use_multiprocessing=use_multiprocessing)
  File "/home/554282/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 646, in _process_inputs
    x, y, sample_weight=sample_weights)
  File "/home/554282/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py", line 2383, in _standardize_user_data
    batch_size=batch_size)
  File "/home/554282/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py", line 2489, in _standardize_tensors
    y, self._feed_loss_fns, feed_output_shapes)
  File "/home/554282/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_utils.py", line 810, in check_loss_and_target_compatibility
    ' while using as loss `' + loss_name + '`. '
ValueError: A target array with shape (8, 1, 3) was passed for an output of shape (None, 3) while using as loss `categorical_crossentropy`. This loss expects targets to have the same shape as the output.

Ответы [ 2 ]

0 голосов
/ 08 февраля 2020

Решенная проблема:

  • Изменена форма метки с (m, 1, 3) на (m, 3) путем удаления .reshape(1,3) из процесса кодирования в одно касание
0 голосов
/ 08 февраля 2020

Функция custom_one_hot возвращает массив [M, 1, 3]. Вы должны изменить это на [M, 3], поскольку выход CNN равен [M, 3]. M здесь размер партии.

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