Keras：训练出错。尺寸不是预期的

Question

我正在开发一个项目，我正在尝试微调 VGG-FACE 模型的最后一层。 但是每次我尝试进行拟合阶段时，我都会遇到同样的错误：

Traceback (most recent call last):
  File "freeze_2.py", line 258, in <module>
    model=entrenamos_modelo('vggface_weights_tensorflow.h5')  
  File "freeze_2.py", line 168, in entrenamos_modelo
    model2.fit(train_data,label, nb_epoch=nb_epoch, batch_size=64)

  File "/imatge/psereno/workspace/venv-tfg/local/lib/python2.7/site-packages/keras/engine/training.py", line 1057, in fit
    batch_size=batch_size)

  File "/imatge/psereno/workspace/venv-tfg/local/lib/python2.7/site-packages/keras/engine/training.py", line 984, in _standardize_user_data
    exception_prefix='model input')

  File "/imatge/psereno/workspace/venv-tfg/local/lib/python2.7/site-packages/keras/engine/training.py", line 111, in standardize_input_data
    str(array.shape))

Exception: Error when checking model input: expected input_2 to have shape (None, 3, 224, 224) but got array with shape (1576, 4096, 1, 1)

这是我正在使用的代码：

from keras.models import Model
from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dropout, Activation
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.optimizers import SGD
from keras.layers import merge
from keras.models import Merge
import cv2, numpy as np
from PIL import Image
from keras.preprocessing.image import ImageDataGenerator

'''OJOOOOO: PARA QUE FUNCIONE BIEN:
nano ~/.keras/keras.json
{
"image_dim_ordering": "th",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}
'''

def bottleneck():
    datagen = ImageDataGenerator(rescale=1.)
    generator = datagen.flow_from_directory(train_data_dir,
                                        target_size=(img_width, img_height),
                                        batch_size=32,
                                        class_mode=None,
                                        shuffle=False)

    pad1_1 = ZeroPadding2D(padding=(1, 1), name='in_train')(img)
    conv1_1 = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')(pad1_1)
    pad1_2 = ZeroPadding2D(padding=(1, 1))(conv1_1)
    conv1_2 = Convolution2D(64, 3, 3, activation='relu', name='conv1_2')(pad1_2)
    pool1 = MaxPooling2D((2, 2), strides=(2, 2))(conv1_2)

    pad2_1 = ZeroPadding2D((1, 1), trainable=False)(pool1)
    conv2_1 = Convolution2D(128, 3, 3, activation='relu', name='conv2_1')(pad2_1)
    pad2_2 = ZeroPadding2D((1, 1), trainable=False)(conv2_1)
    conv2_2 = Convolution2D(128, 3, 3, activation='relu', name='conv2_2')(pad2_2)
    pool2 = MaxPooling2D((2, 2), strides=(2, 2), trainable=False)(conv2_2)

    pad3_1 = ZeroPadding2D((1, 1))(pool2)
    conv3_1 = Convolution2D(256, 3, 3, activation='relu', name='conv3_1')(pad3_1)
    pad3_2 = ZeroPadding2D((1, 1))(conv3_1)
    conv3_2 = Convolution2D(256, 3, 3, activation='relu', name='conv3_2')(pad3_2)
    pad3_3 = ZeroPadding2D((1, 1))(conv3_2)
    conv3_3 = Convolution2D(256, 3, 3, activation='relu', name='conv3_3')(pad3_3)
    pool3 = MaxPooling2D((2, 2), strides=(2, 2), trainable=False)(conv3_3)

    pad4_1 = ZeroPadding2D((1, 1))(pool3)
    conv4_1 = Convolution2D(512, 3, 3, activation='relu', name='conv4_1')(pad4_1)
    pad4_2 = ZeroPadding2D((1, 1))(conv4_1)
    conv4_2 = Convolution2D(512, 3, 3, activation='relu', name='conv4_2')(pad4_2)
    pad4_3 = ZeroPadding2D((1, 1))(conv4_2)
    conv4_3 = Convolution2D(512, 3, 3, activation='relu', name='conv4_3')(pad4_3)
    pool4 = MaxPooling2D((2, 2), strides=(2, 2))(conv4_3)

    pad5_1 = ZeroPadding2D((1, 1))(pool4)
    conv5_1 = Convolution2D(512, 3, 3, activation='relu', name='conv5_1')(pad5_1)
    pad5_2 = ZeroPadding2D((1, 1))(conv5_1)
    conv5_2 = Convolution2D(512, 3, 3, activation='relu', name='conv5_2') (pad5_2)
    pad5_3 = ZeroPadding2D((1, 1))(conv5_2)
    conv5_3 = Convolution2D(512, 3, 3, activation='relu', name='conv5_3')(pad5_3)
    pool5 = MaxPooling2D((2, 2), strides=(2, 2))(conv5_3)
    fc6 = Convolution2D(4096, 7, 7, activation='relu', name='fc6')(pool5)
    fc6_drop = Dropout(0.5)(fc6)

    model = Model(input=img, output=fc6_drop)
    bottleneck_features_train = model.predict_generator(generator, nb_train_samples)
    np.save(open('features.npy', 'w'), bottleneck_features_train)


def entrenamos_modelo(weights_path=None):

    train_data = np.load(open('features.npy'))
    print(train_data.shape)

    train_labels = np.array(
    [0] * (nb_train_samples / 8) + [1] * (nb_train_samples / 8) + [2] * (nb_train_samples / 8) + [3] * (
        nb_train_samples / 8) + [4] * (nb_train_samples / 8) + [5] * (nb_train_samples / 8) + [6] * (
        nb_train_samples / 8) + [7] * (nb_train_samples / 8))

    lbl1 = np.array([[1, 0, 0, 0, 0, 0, 0, 0], ] * 197)
    lbl2 = np.array([[0, 1, 0, 0, 0, 0, 0, 0], ] * 197)
    lbl3 = np.array([[0, 0, 1, 0, 0, 0, 0, 0], ] * 197)
    lbl4 = np.array([[0, 0, 0, 1, 0, 0, 0, 0], ] * 197)
    lbl5 = np.array([[0, 0, 0, 0, 1, 0, 0, 0], ] * 197)
    lbl6 = np.array([[0, 0, 0, 0, 0, 1, 0, 0], ] * 197)
    lbl7 = np.array([[0, 0, 0, 0, 0, 0, 1, 0], ] * 197)
    lbl8 = np.array([[0, 0, 0, 0, 0, 0, 0, 1], ] * 197)
    label = np.concatenate([lbl1, lbl2, lbl3, lbl4, lbl5, lbl6, lbl7, lbl8])
    '''train_labels --> loss='sparse_categorical_crossentropy'
       labels --> loss='categorical_crossentropy'
    '''

    #MODEL VGG (the old model)
    pad1_1 = ZeroPadding2D(padding=(1, 1), trainable=False, input_shape=(4096, 1, 1), name='in_train')(img)
    conv1_1 = Convolution2D(64, 3, 3, activation='relu', name='conv1_1', trainable=False)(pad1_1)
    pad1_2 = ZeroPadding2D(padding=(1, 1), trainable=False)(conv1_1)
    conv1_2 = Convolution2D(64, 3, 3, activation='relu', name='conv1_2', trainable=False)(pad1_2)
    pool1 = MaxPooling2D((2, 2), strides=(2, 2), trainable=False)(conv1_2)

    pad2_1 = ZeroPadding2D((1, 1), trainable=False)(pool1)
    conv2_1 = Convolution2D(128, 3, 3, activation='relu', name='conv2_1', trainable=False)(pad2_1)
    pad2_2 = ZeroPadding2D((1, 1), trainable=False)(conv2_1)
    conv2_2 = Convolution2D(128, 3, 3, activation='relu', name='conv2_2', trainable=False)(pad2_2)
    pool2 = MaxPooling2D((2, 2), strides=(2, 2), trainable=False)(conv2_2)

    pad3_1 = ZeroPadding2D((1, 1), trainable=False)(pool2)
    conv3_1 = Convolution2D(256, 3, 3, activation='relu', name='conv3_1', trainable=False)(pad3_1)
    pad3_2 = ZeroPadding2D((1, 1), trainable=False)(conv3_1)
    conv3_2 = Convolution2D(256, 3, 3, activation='relu', name='conv3_2', trainable=False)(pad3_2)
    pad3_3 = ZeroPadding2D((1, 1), trainable=False)(conv3_2)
    conv3_3 = Convolution2D(256, 3, 3, activation='relu', name='conv3_3', trainable=False)(pad3_3)
    pool3 = MaxPooling2D((2, 2), strides=(2, 2), trainable=False)(conv3_3)

    pad4_1 = ZeroPadding2D((1, 1), trainable=False)(pool3)
    conv4_1 = Convolution2D(512, 3, 3, activation='relu', name='conv4_1', trainable=False)(pad4_1)
    pad4_2 = ZeroPadding2D((1, 1), trainable=False)(conv4_1)
    conv4_2 = Convolution2D(512, 3, 3, activation='relu', name='conv4_2', trainable=False)(pad4_2)
    pad4_3 = ZeroPadding2D((1, 1), trainable=False)(conv4_2)
    conv4_3 = Convolution2D(512, 3, 3, activation='relu', name='conv4_3', trainable=False)(pad4_3)
    pool4 = MaxPooling2D((2, 2), strides=(2, 2) , trainable=False)(conv4_3)

    pad5_1 = ZeroPadding2D((1, 1) , trainable=False)(pool4)
    conv5_1 = Convolution2D(512, 3, 3, activation='relu', name='conv5_1', trainable=False)(pad5_1)
    pad5_2 = ZeroPadding2D((1, 1), trainable=False)(conv5_1)
    conv5_2 = Convolution2D(512, 3, 3, activation='relu', name='conv5_2', trainable=False)(pad5_2)
    pad5_3 = ZeroPadding2D((1, 1), trainable=False)(conv5_2)
    conv5_3 = Convolution2D(512, 3, 3, activation='relu', name='conv5_3', trainable=False)(pad5_3)
    pool5 = MaxPooling2D((2, 2), strides=(2, 2), trainable=False)(conv5_3)

    fc6 = Convolution2D(4096, 7, 7, activation='relu', name='fc6', trainable=False)(pool5)
    fc6_drop = Dropout(0.5)(fc6)

    #We TRAIN this layer
    fc7 = Convolution2D(4096, 1, 1, activation='relu', name='fc7', trainable=False)(fc6_drop)
    fc7_drop = Dropout(0.5)(fc7)
    fc8 = Convolution2D(2622, 1, 1, name='fc8', trainable=False)(fc7_drop)
    flat = Flatten()(fc8)
    out = Activation('softmax')(flat)
    model = Model(input=img, output=out)

    #We load the weight of the old model so when we construct ours we dont have to retrain all of it.
    if weights_path:
        model.load_weights(weights_path)

    # We construct our new model: first 14 layers of the old + two new ones. The new FC has to be trained and the Softmax layer too.
    fc7_n = Convolution2D(4096, 1, 1, activation='relu', name='fc7_n', trainable=True,  input_shape=train_data.shape[1:])(fc6_drop)
    fc7_drop_n = Dropout(0.5)(fc7_n)
    fc8_n = Convolution2D(8, 1, 1, name='fc8_n', trainable=False)(fc7_drop_n)
    flat_n = Flatten(name='flat_n')(fc8_n)
    out_n = Activation('softmax')(flat_n)
    model2 = Model(input=img, output=out_n)
    #model2.summary()


    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    model2.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])

    '''train_labels --> loss='sparse_categorical_crossentropy'
       labels --> loss='categorical_crossentropy'
    '''

    model2.fit(train_data,label, nb_epoch=nb_epoch, batch_size=64)
    print('Model Trained')

    #We save the weights so we can load them in our model
    model2.save_weights(pesos_entrenados)  # always save your weights after training or during training

    #We have two options: 1) Return the model here or in the vgg_trained_model Function
    return model2



if __name__ == "__main__":
    im = Image.open('A.J._Buckley.jpg')
    im = im.resize((224, 224))
    im = np.array(im).astype(np.float32)
    im = im.transpose((2, 0, 1))
    im = np.expand_dims(im, axis=0)

    # For the training stage
    img_width, img_height = 224, 224
    img = Input(shape=(3, img_height, img_width))
    train_data_dir = 'merge/train'
    pesos_entrenados='Modelo_Reentrenado.h5'
    # validation_data_dir = 'data/validation'
    nb_train_samples = 1576  # 197 per class and we have 8 classes (8 emotions)
    nb_validation_samples = 0
    nb_epoch = 20


    # Stages to construct the model
    bottleneck()   #Reduce the computational cost
    model=entrenamos_modelo('vggface_weights_tensorflow.h5')  #Construction of the model

    #model.summary()

    out = model.predict(im)
    print(out[0][0])

“vggface_weights_tensorflow.h5”是从 theano 中的模型权重到 tensorflow 的转换。我使用以下脚本来转换权重：

model = Model(input=img, output=out)
weights_path = 'vgg-face-keras.h5'
model.load_weights(weights_path)
ops = []

for layer in model.layers:
   if layer.__class__.__name__ in ['Convolution1D', 'Convolution2D', 'Convolution3D', 'AtrousConvolution2D']:
      original_w = K.get_value(layer.W)
      converted_w = convert_kernel(original_w)
      ops.append(tf.assign(layer.W, converted_w).op)
K.get_session().run(ops)
model.save_weights('vggface_weights_tensorflow.h5')

原始脚本来自这里：

https://gist.github.com/EncodeTS/6bbe8cb8bebad7a672f0d872561782d9

如果有人知道如何解决问题，我将永远感激不尽。

Answer 1

您是否调整了所有图像的大小，使其为 224 x 224？

该错误看起来像是模型需要一个任意大小的图像列表，具有 3 个通道 (RGB)、224 宽和 224 高。它看起来更像是您的数据输入，而不是 Keras/代码的任何内容。尝试确保您的目录和数据按照模型的预期设置？