TensorFlow 中带有 image_generator 的 input_shape答案

【问题标题】：input_shape with image_generator in TensorflowTensorFlow 中带有 image_generator 的 input_shape
【发布时间】：2020-04-14 19:03:54
【问题描述】：

我正在尝试在 Tensorflow 2.X 中使用这种方法来加载不适合内存的大型数据集。

我有一个包含 X 个子文件夹的文件夹，其中包含图像。每个子文件夹都是一个类。

\dataset
    -\class1
        -img1_1.jpg
        -img1_2.jpg
        -...
    -\classe2
        -img2_1.jpg
        -img2_2.jpg
        -...

我从我的文件夹中创建我的数据生成器，如下所示：

train_data_gen = image_generator.flow_from_directory(directory="path\\to\\dataset",
                                                     batch_size=100,
                                                     shuffle=True,
                                                     target_size=(100, 100), # Image H x W
                                                     classes=list(CLASS_NAMES)) # list of folder/class names ["class1", "class2", ...., "classX"]

找到属于 2 个类别的 629 张图片。

我做了一个较小的数据集来测试管道。 2 类中只有 629 幅图像。现在我可以像这样创建一个虚拟模型：

model = tf.keras.Sequential()
model.add(Dense(1, activation=activation, input_shape=(100, 100, 3))) # only 1 layer of 1 neuron
model.add(Dense(2)) # 2classes
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=['categorical_accuracy'])

编译后我会尝试拟合这个虚拟模型：

STEPS_PER_EPOCH = np.ceil(image_count / batch_size) # 629 / 100
model.fit_generator(generator=train_data_gen , steps_per_epoch=STEPS_PER_EPOCH, epochs=2, verbose=1)

1/7 [===>..........................] - ETA: 2s - loss: 1.1921e-07 - categorical_accuracy: 0.9948
2/7 [=======>......................] - ETA: 1s - loss: 1.1921e-07 - categorical_accuracy: 0.5124
3/7 [===========>..................] - ETA: 0s - loss: 1.1921e-07 - categorical_accuracy: 0.3449
4/7 [================>.............] - ETA: 0s - loss: 1.1921e-07 - categorical_accuracy: 0.2662
5/7 [====================>.........] - ETA: 0s - loss: 1.1921e-07 - categorical_accuracy: 0.2130
6/7 [========================>.....] - ETA: 0s - loss: 1.1921e-07 - categorical_accuracy: 0.1808

2020-04-14 20:39:48.629203：W tensorflow/core/framework/op_kernel.cc:1610] 无效参数：ValueError：generator 产生了一个形状为 (29, 100, 100, 3) 的元素，其中需要一个形状为 (100, 100, 100, 3) 的元素。

据我了解，最后一批与前一批的形状不同。所以它崩溃了。我尝试指定batch_input_shape。

model.add(Dense(1, activation=activation, batch_input_shape=(None, 100, 100, 3)))

我发现 here 我应该使用 None 不指定批处理中的元素数量，以便它可以是动态的。但没有成功。

编辑：从评论中我有两个错误：

输出形状错误。我错过了模型中的展平层。
上一个链接确实适用于扁平层的校正
缺少一些代码，我实际上给fit_generator 提供了tf.data.Dataset.from_generator，但我在这里给了image_generator.flow_from_directory。

这是最终代码：

train_data_gen = image_generator.flow_from_directory(directory="path\\to\\dataset",
    batch_size=1000,
    shuffle=True,
    target_size=(100, 100),
    classes=list(CLASS_NAMES))

train_dataset = tf.data.Dataset.from_generator(
    lambda: train_data_gen,
    output_types=(tf.float32, tf.float32),
    output_shapes=([None, x, y, 3],
    [None, len(CLASS_NAMES)]))

model = tf.keras.Sequential()
model.add(Flatten(batch_input_shape=(None, 100, 100, 3)))
model.add(Dense(1, activation=activation))
model.add(Dense(2))
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=['categorical_accuracy'])

STEPS_PER_EPOCH = np.ceil(image_count / batch_size) # 629 / 100
model.fit_generator(generator=train_data_gen , steps_per_epoch=STEPS_PER_EPOCH, epochs=2, verbose=1)

【问题讨论】：

您确定您共享的是最新的代码吗？你给的那个甚至不应该运行。您的最后一个 Dense 层会产生 (None, 100, 100, 2) 大小的输出（您没有 Flatten() 层），但您的标签是 (None, 2)，这应该会引发错误（对我来说确实如此） )。
确实你对输出形状是正确的。我也没有从 image_generator 中提供有关数据集的所有代码。如果我找到它，我会尝试修复并分享我的答案。

标签： tensorflow2.0 tensorflow-datasets

【解决方案1】：

为了社区的利益，我在这里解释一下，如何在 Tensorflow 中使用 image_generator 和 input_shape (100, 100, 3) 使用 dogs vs cats 数据集

如果我们没有选择正确的批量大小，那么模型有可能在第一个 epoch 之后立即被击中，因此我将使用 how to choose batch_size ? 开始我的解释

我们通常观察到batch size 是power of 2，这是因为优化的矩阵运算库的有效工作。这在this 研究论文中有进一步阐述。

查看this 博客，该博客描述了如何选择正确的batch size，同时比较不同批量大小对 CIFAR-10 数据集的accuracy 的影响。

这是带有输出的端到端工作代码

import os
import numpy as np
from keras import layers
import pandas as pd
from tensorflow.keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from tensorflow.keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras import regularizers, optimizers
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator

import keras.backend as K

K.set_image_data_format('channels_last')

train_dir  = '/content/drive/My Drive/Dogs_Vs_Cats/train'
test_dir = '/content/drive/My Drive/Dogs_Vs_Cats/test'

img_width, img_height = 100, 100
input_shape = img_width, img_height, 3

train_samples = 2000
test_samples = 1000
epochs = 30
batch_size = 32

train_datagen = ImageDataGenerator(
    rescale = 1. /255,
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True)

test_datagen = ImageDataGenerator(
    rescale = 1. /255)

train_data = train_datagen.flow_from_directory(
    train_dir,
    target_size = (img_width, img_height),
    batch_size = batch_size,
    class_mode = 'binary')

test_data = test_datagen.flow_from_directory(
    test_dir,
    target_size = (img_width, img_height),
    batch_size = batch_size,
    class_mode = 'binary')


model = Sequential()

model.add(Conv2D(32, (7, 7), strides = (1, 1), input_shape = input_shape))
model.add(BatchNormalization(axis = 3))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (7, 7), strides = (1, 1)))
model.add(BatchNormalization(axis = 3))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.summary() 


model.compile(loss = 'binary_crossentropy',
            optimizer = 'rmsprop',
            metrics = ['accuracy'])

model.fit_generator(
        train_data,
        steps_per_epoch = train_samples//batch_size,
        epochs = epochs,
        validation_data = test_data,
        verbose = 1,
        validation_steps = test_samples//batch_size)

输出：

Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Model: "sequential_5"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_8 (Conv2D)            (None, 94, 94, 32)        4736      
_________________________________________________________________
batch_normalization_8 (Batch (None, 94, 94, 32)        128       
_________________________________________________________________
activation_8 (Activation)    (None, 94, 94, 32)        0         
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 47, 47, 32)        0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 41, 41, 64)        100416    
_________________________________________________________________
batch_normalization_9 (Batch (None, 41, 41, 64)        256       
_________________________________________________________________
activation_9 (Activation)    (None, 41, 41, 64)        0         
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 20, 20, 64)        0         
_________________________________________________________________
flatten_4 (Flatten)          (None, 25600)             0         
_________________________________________________________________
dense_11 (Dense)             (None, 64)                1638464   
_________________________________________________________________
dropout_4 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_12 (Dense)             (None, 1)                 65        
=================================================================
Total params: 1,744,065
Trainable params: 1,743,873
Non-trainable params: 192
_________________________________________________________________
Epoch 1/30
62/62 [==============================] - 14s 225ms/step - loss: 1.8307 - accuracy: 0.4853 - val_loss: 0.6931 - val_accuracy: 0.5000
Epoch 2/30
62/62 [==============================] - 14s 226ms/step - loss: 0.7085 - accuracy: 0.4832 - val_loss: 0.6931 - val_accuracy: 0.5010
Epoch 3/30
62/62 [==============================] - 14s 218ms/step - loss: 0.6955 - accuracy: 0.5300 - val_loss: 0.6894 - val_accuracy: 0.5292
Epoch 4/30
62/62 [==============================] - 14s 221ms/step - loss: 0.6938 - accuracy: 0.5407 - val_loss: 0.7309 - val_accuracy: 0.5262
Epoch 5/30
62/62 [==============================] - 14s 218ms/step - loss: 0.6860 - accuracy: 0.5498 - val_loss: 0.6776 - val_accuracy: 0.5665
Epoch 6/30
62/62 [==============================] - 13s 216ms/step - loss: 0.7027 - accuracy: 0.5407 - val_loss: 0.6895 - val_accuracy: 0.5101
Epoch 7/30
62/62 [==============================] - 13s 216ms/step - loss: 0.6852 - accuracy: 0.5528 - val_loss: 0.6567 - val_accuracy: 0.5887
Epoch 8/30
62/62 [==============================] - 13s 217ms/step - loss: 0.6772 - accuracy: 0.5427 - val_loss: 0.6643 - val_accuracy: 0.5847
Epoch 9/30
62/62 [==============================] - 13s 217ms/step - loss: 0.6709 - accuracy: 0.5534 - val_loss: 0.6623 - val_accuracy: 0.5887
Epoch 10/30
62/62 [==============================] - 14s 219ms/step - loss: 0.6579 - accuracy: 0.5711 - val_loss: 0.6614 - val_accuracy: 0.6058
Epoch 11/30
62/62 [==============================] - 13s 218ms/step - loss: 0.6591 - accuracy: 0.5625 - val_loss: 0.6594 - val_accuracy: 0.5454
Epoch 12/30
62/62 [==============================] - 13s 216ms/step - loss: 0.6419 - accuracy: 0.5767 - val_loss: 1.1041 - val_accuracy: 0.5161
Epoch 13/30
62/62 [==============================] - 13s 215ms/step - loss: 0.6479 - accuracy: 0.5783 - val_loss: 0.6441 - val_accuracy: 0.5837
Epoch 14/30
62/62 [==============================] - 13s 216ms/step - loss: 0.6373 - accuracy: 0.5899 - val_loss: 0.6427 - val_accuracy: 0.6310
Epoch 15/30
62/62 [==============================] - 13s 215ms/step - loss: 0.6203 - accuracy: 0.6133 - val_loss: 0.7390 - val_accuracy: 0.6220
Epoch 16/30
62/62 [==============================] - 13s 217ms/step - loss: 0.6277 - accuracy: 0.6362 - val_loss: 0.6649 - val_accuracy: 0.5786
Epoch 17/30
62/62 [==============================] - 13s 215ms/step - loss: 0.6155 - accuracy: 0.6316 - val_loss: 0.9823 - val_accuracy: 0.5484
Epoch 18/30
62/62 [==============================] - 14s 222ms/step - loss: 0.6056 - accuracy: 0.6408 - val_loss: 0.6333 - val_accuracy: 0.6048
Epoch 19/30
62/62 [==============================] - 14s 218ms/step - loss: 0.6025 - accuracy: 0.6529 - val_loss: 0.6514 - val_accuracy: 0.6442
Epoch 20/30
62/62 [==============================] - 13s 215ms/step - loss: 0.6149 - accuracy: 0.6423 - val_loss: 0.6373 - val_accuracy: 0.6048
Epoch 21/30
62/62 [==============================] - 13s 215ms/step - loss: 0.6030 - accuracy: 0.6519 - val_loss: 0.6086 - val_accuracy: 0.6573
Epoch 22/30
62/62 [==============================] - 13s 217ms/step - loss: 0.5936 - accuracy: 0.6865 - val_loss: 1.0677 - val_accuracy: 0.5605
Epoch 23/30
62/62 [==============================] - 13s 214ms/step - loss: 0.5964 - accuracy: 0.6728 - val_loss: 0.7927 - val_accuracy: 0.5877
Epoch 24/30
62/62 [==============================] - 13s 215ms/step - loss: 0.5866 - accuracy: 0.6707 - val_loss: 0.6116 - val_accuracy: 0.6421
Epoch 25/30
62/62 [==============================] - 13s 214ms/step - loss: 0.5933 - accuracy: 0.6662 - val_loss: 0.8282 - val_accuracy: 0.6048
Epoch 26/30
62/62 [==============================] - 13s 214ms/step - loss: 0.5705 - accuracy: 0.6885 - val_loss: 0.5806 - val_accuracy: 0.6966
Epoch 27/30
62/62 [==============================] - 14s 218ms/step - loss: 0.5709 - accuracy: 0.7017 - val_loss: 1.2404 - val_accuracy: 0.5333
Epoch 28/30
62/62 [==============================] - 13s 216ms/step - loss: 0.5691 - accuracy: 0.7104 - val_loss: 0.6136 - val_accuracy: 0.6442
Epoch 29/30
62/62 [==============================] - 13s 215ms/step - loss: 0.5627 - accuracy: 0.7048 - val_loss: 0.6936 - val_accuracy: 0.6613
Epoch 30/30
62/62 [==============================] - 13s 214ms/step - loss: 0.5714 - accuracy: 0.6941 - val_loss: 0.5872 - val_accuracy: 0.6825

【讨论】：

@BeGreen，您能否确认您的问题是否已通过上面给出的示例代码得到解决。如果没有，请发布您面临的错误，我很乐意为您提供帮助。谢谢！