我正在 EMNIST 上运行一个模型(128x128 灰度图像),但我无法理解如何将数据正确加载到 Tensorflow 以进行建模。
我一直在关注 TensorFlow 提供的花朵示例 (https://www.tensorflow.org/hub/tutorials/image_feature_vector) 除了 CNN 结构,直到突然 model.fit() 失败并出现错误
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Input 0 of layer conv2d_120 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape [None, 64, 64, 3]
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
batch_size = 32
image_w = 64
image_h = 64
seed = 123
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找到属于 10 个类的 10160 个文件。
使用 8128 个文件进行训练。
找到属于 10 个类的 10160 个文件。
使用 2032 文件进行验证。
data_dir = 'B:\Datasets\EMNIST Digital Number & Digits\OriginalDigits'
train_df = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=seed,
image_size=(image_w,image_h),
batch_size=batch_size)
val_df = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation", #Same exact code block ... this is the only line of difference
seed=seed,
image_size=(image_w,image_h),
batch_size=batch_size)
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for images, labels in train_df.take(1): #Take subsets the dataset into at most __1__ element (Seems to randomly create it)
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(labels[i].numpy().astype("str"))
plt.axis("off")
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['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] 10
class_labels = train_df.class_names
num_classes = len(class_labels)
print(class_labels,num_classes)
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_df_modeling = train_df.cache().shuffle(len(train_df)) #Load training data into memory cache + shuffle all 10160 images
val_df_modeling = val_df.cache().shuffle(len(train_df)) #Load validation data into memory cache
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模型:“顺序”
_________________________________________________________________
层(类型)输出形状参数#
================================================== ================
重新缩放 (Rescaling) (None, 64, 64, 1) 0
_________________________________________________________________
conv2d (Conv2D) (无, 64, 64, 64) 640
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 32, 32, 64) 0
_________________________________________________________________
conv2d_1 (Conv2D)(无、32、32、128)73856
_________________________________________________________________
conv2d_2 (Conv2D)(无、32、32、128)147584
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 16, 16, 128) 0
_________________________________________________________________
conv2d_3 (Conv2D)(无、16、16、128)147584
_________________________________________________________________
conv2d_4 (Conv2D) (None, 16, 16, 128) 147584
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 8, 8, 128) 0
_________________________________________________________________
展平(Flatten)(无,8192)0
_________________________________________________________________
密集(Dense)(无,256)2097408
_________________________________________________________________
辍学(辍学)(无,256)0
_________________________________________________________________
密集_1(密集)(无,128)32896
_________________________________________________________________
dropout_1(辍学)(无,128)0
_________________________________________________________________
密集_2(密集)(无,64)8256
_________________________________________________________________
dropout_2(辍学)(无,64)0
_________________________________________________________________
密集_3(密集)(无,10)650
================================================== ================
总参数:2,656,458
可训练参数:2,656,458
不可训练的参数:0
#Model from https://www.kaggle.com/henseljahja/simple-tensorflow-cnn-98-8
model = keras.models.Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(image_h, image_w, 1)), #(64,64,1)
layers.Conv2D(64, 7, padding='same', activation='relu'),
layers.GaussianNoise(0.2),
layers.MaxPooling2D(pool_size=2),
layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding="SAME"),
layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding="SAME"),
layers.MaxPooling2D(pool_size=2),
layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding="SAME"),
layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding="SAME"),
layers.MaxPooling2D(pool_size=2),
layers.Flatten(),
layers.Dense(units=256, activation='relu'),
layers.Dropout(0.5),
layers.Dense(units=128, activation='relu'),
layers.Dropout(0.5),
layers.Dense(units=64, activation='relu'),
layers.Dropout(0.5),
keras.layers.Dense(num_classes, activation='softmax'), #10 outputs [0,1,2,3,4,5,6,7,8,9]
])
model.summary()
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ValueError: 层 conv2d 的输入 0 与层不兼容:输入形状的预期轴 -1 具有值 1 但接收到形状为 [None, 64, 64, 3] 的输入
我知道我的问题与形状有关,并且 [None, 64, 64, 3] 是 [batch_size, width, height, channels] 但我有以下问题:
model.compile(
loss="sparse_categorical_crossentropy",
optimizer = 'nadam',
metrics=['accuracy']
)
result = model.fit(train_df_modeling,
validation_data=val_df_modeling,
epochs=20,
verbose=1)
? Conv2D 层不应该期待图像吗? 答案 0 :(得分:0)
嗯……在输入关于我遇到的问题的最后一部分的过程中,我找到了问题 2 的解决方案。
我的数据(虽然是灰度数据)被 Tensorflow 读取为 RGB,因为我从未指定。
读取灰度数据
文档:https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image_dataset_from_directory
感兴趣的参数:color_mode='grayscale'
只需要更改 1 块代码(2 个变量)
data_dir = 'B:\Datasets\EMNIST Digital Number & Digits\OriginalDigits'
train_df = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=seed,
image_size=(image_w,image_h),
batch_size=batch_size,
color_mode='grayscale') #<---- This is was the missing link
val_df = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=seed,
image_size=(image_w,image_h),
batch_size=batch_size,
color_mode='grayscale') #<---- This is was the missing link
虽然这个解决方案修复了模型并允许代码执行......有人能回答问题 #1 吗?我仍然很好奇为什么它认为它需要输入 have value 1
当我认为输入应该是图像时。