在Keras训练物体检测模型时，张量形状不相容的问题

我正在尝试将基本分类模型（https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/）扩展为单个对象的简单对象检测模型 .

分类模型简单地对图像中的手写数字进行分类，其中数字填充大部分图像 . 要为对象检测创建有意义的数据集，我使用MNIST数据集作为基础，并通过以下步骤将其转换为新数据集

• 将图像画布大小从28x28增加到100x100

• 将手写数字移动到100x100图像中的随机位置

• 创建一个地面实况边界框

图1：步骤1和2的图示 .

图2：一些产生的地面真实边界框 .

模型的输出向量受YOLO定义的启发，但对于单个对象：

y = [p, x, y, w, h, c0, ..., c9]

其中p =对象的概率，（x，y，w，h）=边界框中心，宽度和高度作为图像大小的分数，c0-c9 =类概率（每个数字一个） .

因此，为了将分类模型更改为对象检测模型，我只需将最后一个softmax层替换为具有15个节点的完全连接层（ y 中的每个值一个），并编写一个自定义损失函数，可以将预测与基础事实进行比较 .

但是，当我尝试训练模型时，我得到了神秘的错误 tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [15] vs. [200] ，其中 [15] 是我最后一层中的节点数， [200] 是我为训练指定的批量大小（我通过更改值并再次运行来验证这一点） . 它们不能合理地相同，所以我想我在模型中的张量尺寸方面遗漏了一些重要的东西，但我无法弄清楚是什么 .

注意：我对批次的理解是模型在训练期间一次处理多少样本（图像） . 因此，批量大小应该是训练数据大小的偶数部分是合理的 . 但是没有什么可以将它连接到模型中的输出节点数量 .

任何帮助表示赞赏 .

以下是完整的代码：

import numpy as np

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras import backend as K


def increase_image_size(im_set, new_size):
    num_images = im_set.shape[0]
    orig_size = im_set[0].shape[0]
    im_stack = np.zeros((num_images, new_size, new_size), dtype='uint8')

    # Put MNIST digits at random positions in new images
    for i in range(num_images):
        x0 = int(np.random.random() * (new_size - orig_size - 1))
        y0 = int(np.random.random() * (new_size - orig_size - 1))
        x1 = x0 + orig_size
        y1 = y0 + orig_size

        im_stack[i, y0:y1, x0:x1] = im_set[i]

    return im_stack


# Get bounding box annotations from images and object labels
def get_image_annotations(X_train, y_train):
    num_images = len(X_train)
    annotations = np.zeros((num_images, 15), dtype='float')
    for i in range(num_images):
        annotations[i] = get_image_annotation(X_train[i], y_train[i])
    return annotations


def get_image_annotation(X, y):
    sz_y, sz_x = X.shape

    y_indices, x_indices = np.where(X > 0)

    y_min = max(np.min(y_indices) - 1, 0)
    y_max = min(np.max(y_indices) + 1, sz_y)
    x_min = max(np.min(x_indices) - 1, 0)
    x_max = min(np.max(x_indices) + 1, sz_x)

    bb_x = (x_min + x_max) / 2.0 / sz_x
    bb_y = (y_min + y_max) / 2.0 / sz_y

    bb_w = (x_max - x_min) / sz_x
    bb_h = (y_max - y_min) / sz_y

    classes = np.zeros(10, dtype='float')
    classes[y] = 1

    output = np.concatenate(([1, bb_x, bb_y, bb_w, bb_h], classes))
    return output


def custom_cost_function(y_true, y_pred):
    p_p = y_pred[0]
    x_p = y_pred[1]
    y_p = y_pred[2]
    w_p = y_pred[3]
    h_p = y_pred[4]

    p_t = y_true[0]
    x_t = y_true[1]
    y_t = y_true[2]
    w_t = y_true[3]
    h_t = y_true[4]

    c_pred = y_pred[5:]
    c_true = y_true[5:]

    c1 = K.sum((c_pred - c_true) * (c_pred - c_true))
    c2 = (x_p - x_t) * (x_p - x_t) + (y_p - y_t) * (y_p - y_t) \
         + (K.sqrt(w_p) - K.sqrt(w_t)) * (K.sqrt(w_p) - K.sqrt(w_t)) \
         + (K.sqrt(h_p) - K.sqrt(h_t)) * (K.sqrt(h_p) - K.sqrt(h_t))

    lambda_class = 1.0
    lambda_coord = 1.0

    return lambda_class * c1 + lambda_coord * c2


def baseline_model():
    # create model
    model = Sequential()
    model.add(Conv2D(32, (5, 5), input_shape=(1, 100, 100), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(15, activation='linear'))
    # Compile model
    model.compile(loss=custom_cost_function, optimizer='adam', metrics=['accuracy'])
    return model


def mnist_object_detection():
    K.set_image_dim_ordering('th')

    # fix random seed for reproducibility
    np.random.seed(7)

    # Load data
    print("Loading data")
    (X_train, y_train), (X_test, y_test) = mnist.load_data()

    # Adjust input images
    print("Adjust input images (increasing image sizes and moving digits)")
    X_train = increase_image_size(X_train, 100)
    X_test = increase_image_size(X_test, 100)

    print("Creating annotations")
    y_train_prim = get_image_annotations(X_train, y_train)
    y_test_prim = get_image_annotations(X_test, y_test)
    print("...done")

    # reshape to be [samples][pixels][width][height]
    X_train = X_train.reshape(X_train.shape[0], 1, 100, 100).astype('float32')
    X_test = X_test.reshape(X_test.shape[0], 1, 100, 100).astype('float32')

    # normalize inputs from 0-255 to 0-1
    X_train = X_train / 255
    X_test = X_test / 255

    # build the model
    print("Building model")
    model = baseline_model()
    # Fit the model
    print("Training model")
    model.fit(X_train, y_train_prim, validation_data=(X_test, y_test_prim), epochs=10, batch_size=200, verbose=1)


if __name__ == '__main__':
    mnist_object_detection()

当我运行它时，我收到错误：

/Users/gedda/anaconda3/envs/keras-obj-det/bin/pythonn /Users/gedda/devel/tensorflow/digit-recognition/object_detection_reduced.py
Using TensorFlow backend.
Loading data
Adjust input images (increasing image sizes and moving digits)
Creating annotations
...done
Building model
2018-11-30 13:26:34.030159: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX
2018-11-30 13:26:34.030463: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance.
Training model
Train on 60000 samples, validate on 10000 samples
Epoch 1/3
Traceback (most recent call last):
  File "/Users/gedda/devel/tensorflow/digit-recognition/object_detection_reduced.py", line 140, in <module>
    mnist_object_detection()
  File "/Users/gedda/devel/tensorflow/digit-recognition/object_detection_reduced.py", line 136, in mnist_object_detection
    model.fit(X_train, y_train_prim, validation_data=(X_test, y_test_prim), epochs=3, batch_size=200, verbose=1)
  File "/Users/gedda/anaconda3/envs/keras-obj-det/lib/python3.6/site-packages/keras/engine/training.py", line 1039, in fit
validation_steps=validation_steps)
  File "/Users/gedda/anaconda3/envs/keras-obj-det/lib/python3.6/site-packages/keras/engine/training_arrays.py", line 199, in fit_loop
outs = f(ins_batch)
  File "/Users/gedda/anaconda3/envs/keras-obj-det/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__
return self._call(inputs)
  File "/Users/gedda/anaconda3/envs/keras-obj-det/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call
fetched = self._callable_fn(*array_vals)
  File "/Users/gedda/anaconda3/envs/keras-obj-det/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1439, in __call__
run_metadata_ptr)
  File "/Users/gedda/anaconda3/envs/keras-obj-det/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [15] vs. [200]
     [[{{node training/Adam/gradients/loss/dense_2_loss/mul_7_grad/BroadcastGradientArgs}} = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@training/Adam/gradients/loss/dense_2_loss/mul_7_grad/Reshape"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](training/Adam/gradients/loss/dense_2_loss/mul_7_grad/Shape, training/Adam/gradients/loss/dense_2_loss/mul_7_grad/Shape_1)]]

Process finished with exit code 1