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私はVGG-FACEモデルの最後の層を微調整しようとしているプロジェクトを開発しています。 しかし、毎回私は、フィッティングの段階を実行しようとするとき、私は同じエラー持っている：ケラス：トレーニングでエラーが発生しました。次元は期待通りではありません
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」は、変換されている。ここで
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)
は、私が使用していたコードですテアノフローのモデル荷重からテナントフローまでの距離。私は重みを変換するために、次のスクリプトを使用しています
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')
オリジナルのスクリプトはここからです。
https://gist.github.com/EncodeTS/6bbe8cb8bebad7a672f0d872561782d9
誰もが、私は常に感謝されるという問題を解決する方法を知っていれば。
私がフィットラインにコメントする場合、エラーはありません。だから、エラーはそこにあると仮定しました。 私はKeras（1.1.1）+ Theano（0.9.0.dev4）+ Tensorflow（0.10.0rc0） – Patrisr
を使用しています。モデルの入力は '（なし、3、224、224）'のサイズを持つ 'img'です。しかし、それをサイズ '（1576,4096,1,1）'の 'train_data'に合わせようとします。 – sygi
あなたは//コード内でどこを変更する必要があるのか知っていますか？私はケラで本当に新しいです:( あなたの答えをありがとう – Patrisr