I am developing a project where I am trying to finetune the last layer of a VGG-FACE model.
But everytime when I try to do the fitting stage I've got the same error:
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)
Here is the code I am using:
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])
The 'vggface_weights_tensorflow.h5' are a conversion from the model weights in theano to tensorflow. I have used the following script to convert the weights:
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')
The original scripts are from here:
https://gist.github.com/EncodeTS/6bbe8cb8bebad7a672f0d872561782d9
If anyone knows how to solve the problem I will always be grateful.
Copyright Notice：Content Author:「Patrisr」，Reproduced under the CC 4.0 BY-SA copyright license with a link to the original source and this disclaimer.
Link to original article：https://stackoverflow.com/questions/40692495/keras-error-with-training-dimension-is-not-what-is-expected