类型错误：fit_generator() 得到了一个意外的关键字参数“nb_val_samples”

Question

我试图通过参考这篇文章来制作一个手写分类器：https : //github.com/priya-dwivedi/Deep-Learning/blob/master/handwriting_recognition/English_Writer_Identification.ipynb。
在拟合模型时，我收到一条错误消息，指出 fir_generator 不希望有任何此类参数！此外，虽然错误本身是一个意外的参数错误，但标记显示为类型错误，我想知道我的管道是否有问题。
这是模型。（我排除了错误之后的所有代码，因为它不应该以任何方式相关。如果你觉得它很重要，你可以参考上面链接中的代码）
Tensorflow 版本 - 1.14 ，Keras 版本 - 2.2.4

from __future__ import division
import numpy as np
import os
import glob
from PIL import Image  
from random import *  
from tensorflow.keras.utils 
import to_categorical 
from sklearn.preprocessing 
import LabelEncoder
import matplotlib.pyplot as plt
import matplotlib.image as mpimg 
%matplotlib inline

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Lambda, ELU, Activation, BatchNormalization
from tensorflow.keras.layers import Convolution2D, Cropping2D, ZeroPadding2D, MaxPooling2D 
from tensorflow.keras.optimizers import SGD, Adam, RMSprop
import tensorflow
import tensorflow.keras

# Create sentence writer mapping
#Dictionary with form and writer mapping
d = {}
with open('forms_for_parsing.txt') as f:
    for line in f:
        key = line.split(' ')[0]
        writer = line.split(' ')[1]
        d[key] = writer

tmp = []
target_list = []
path_to_files = os.path.join('datab', '*')
for filename in sorted(glob.glob(path_to_files)):
    tmp.append(filename)
    image_name = filename.split(os.sep)[1]
    file, ext = os.path.splitext(image_name)
    parts = file.split('-')
    form = parts[0] + '-' + parts[1]
    for key in d:
        if key == form:
            target_list.append(str(d[form]))

img_files = np.asarray(tmp)
img_targets = np.asarray(target_list)

# Visualizing the data
for filename in img_files[:3]:
    img=mpimg.imread(filename)
    plt.figure(figsize=(10,10))
    plt.imshow(img, cmap ='gray')

# Label Encode writer names for one hot encoding later
encoder = LabelEncoder()
encoder.fit(img_targets)
encoded_Y = encoder.transform(img_targets)

print(img_files[:5], img_targets[:5], encoded_Y[:5])

#split into test train and validation in ratio 4:1:1

from sklearn.model_selection import train_test_split 
train_files, rem_files, train_targets, rem_targets = train_test_split(
        img_files, encoded_Y, train_size=0.66, random_state=52, shuffle= True)

validation_files, test_files, validation_targets, test_targets = train_test_split(
        rem_files, rem_targets, train_size=0.5, random_state=22, shuffle=True)

print(train_files.shape, validation_files.shape, test_files.shape)
print(train_targets.shape, validation_targets.shape, test_targets.shape)

# Generator function for generating random crops from each sentence

# # Now create generators for randomly cropping 113x113 patches from these images

batch_size = 16 
num_classes = 50

# Start with train generator shared in the class and add image augmentations
def generate_data(samples, target_files,  batch_size=batch_size, factor = 0.1 ):
    num_samples = len(samples)
    from sklearn.utils import shuffle
    while 1: # Loop forever so the generator never terminates
        for offset in range(0, num_samples, batch_size):
            batch_samples = samples[offset:offset+batch_size]
            batch_targets = target_files[offset:offset+batch_size]

            images = []
            targets = []
            for i in range(len(batch_samples)):
                batch_sample = batch_samples[i]
                batch_target = batch_targets[i]
                im = Image.open(batch_sample)
                cur_width = im.size[0]
                cur_height = im.size[1]

                # print(cur_width, cur_height)
                height_fac = 113 / cur_height

                new_width = int(cur_width * height_fac)
                size = new_width, 113

                imresize = im.resize((size), Image.ANTIALIAS)  # Resize so height = 113 while keeping aspect ratio
                now_width = imresize.size[0]
                now_height = imresize.size[1]
                # Generate crops of size 113x113 from this resized image and keep random 10% of crops

                avail_x_points = list(range(0, now_width - 113 ))# total x start points are from 0 to width -113

                # Pick random x%
                pick_num = int(len(avail_x_points)*factor)

                # Now pick
                random_startx = sample(avail_x_points,  pick_num)

                for start in random_startx:
                    imcrop = imresize.crop((start, 0, start+113, 113))
                    images.append(np.asarray(imcrop))
                    targets.append(batch_target)

            # trim image to only see section with road
            X_train = np.array(images)
            y_train = np.array(targets)

            #reshape X_train for feeding in later
            X_train = X_train.reshape(X_train.shape[0], 113, 113, 1)   time , and use -1 
           
            X_train = X_train.astype('float32')  
            X_train /= 255

            #One hot encode y
            y_train = to_categorical(y_train, num_classes) 

            yield shuffle(X_train, y_train) # literraly shuffel 

train_generator = generate_data(train_files, train_targets, batch_size=batch_size, factor = 0.3)
validation_generator = generate_data(validation_files, validation_targets, batch_size=batch_size, factor = 0.3)
test_generator = generate_data(test_files, test_targets, batch_size=batch_size, factor = 0.1)

history_object = model.fit_generator(train_generator, steps_per_epoch= samples_per_epoch1,
                                     validation_data=validation_generator,
                                     nb_val_samples=nb_val_samples, nb_epoch=nb_epoch, verbose=1, callbacks=callbacks_list)

错误日志如下——

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-34-54937a660f6c> in <module>
      1 history_object = model.fit_generator(train_generator, steps_per_epoch= samples_per_epoch1,
      2                                      validation_data=validation_generator,
----> 3                                      nb_val_samples=nb_val_samples, nb_epoch=nb_epoch, verbose=1, callbacks=callbacks_list)

TypeError: fit_generator() got an unexpected keyword argument 'nb_val_samples'

Answer 1

在 Keras 2.0 之后，nb_val_samples关键字编码为validation_steps. 另外，我nb_epoch在您的代码中看到了关键字。它编码为epochs.

如果您不想更改关键字，只需将您的 Keras 降级到 2.0 以下版本