Question

我正在尝试运行简单的代码，但是只训练了一个时期并停止了它。

能给我个解决办法吗？

下面我的完整代码是简单代码，是基本代码。

以下警告最多。

大多数代码都可以正常工作，但是fit函数也可以工作，但是远远不够。

import numpy as np
import os
import PIL
import PIL.Image
import tensorflow as tf
print(tf.__version__)

import pathlib
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file(origin=dataset_url, 
                                   fname='flower_photos', 
                                   untar=True)
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))

batch_size = 32
img_height = 180
img_width = 180

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
    )

num_train = len(np.concatenate([i for x, i in train_ds], axis=0))

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

num_test = len(np.concatenate([i for x, i in val_ds], axis=0))


class_names = train_ds.class_names
print(class_names)

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
    for i in range(9):
        ax = plt.subplot(3, 3, i + 1)
        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[labels[i]])
        plt.axis("off")

for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break

from tensorflow.keras import layers

normalization_layer = tf.keras.layers.experimental.preprocessing.Rescaling(1./255)

normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixels values are now in `[0,1]`.
print(np.min(first_image), np.max(first_image)) 

nor_val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))


# Convolutional Neural Network

import tensorflow as tf
from tensorflow.keras import datasets, layers, models

model  = models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape = (180, 180,3)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64,activation = 'relu'))
model.add(layers.Dense(5,activation = 'softmax'))

model.summary()

model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])

histoy = model.fit(normalized_ds,
        steps_per_epoch= (num_train//batch_size),
        epochs=20,
        shuffle=True,
        validation_data=nor_val_ds,  
        validation_steps = (num_test//batch_size) 
       )

在培训过程中，网络输出如下：

Epoch 1/20
91/91 [==============================] - 63s 696ms/step - loss: 0.8712 - accuracy: 0.6672 - val_loss: 0.9402 - val_accuracy: 0.6293
Epoch 2/20
 1/91 [..............................] - ETA: 0s - loss: 0.8736 - accuracy: 0.6667WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 1820 batches). You may need to use the repeat() function when building your dataset.
 1/91 [..............................] - 4s 4s/step - loss: 0.8736 - accuracy: 0.6667 - val_loss: 0.9902 - val_accuracy: 0.6179

输入耗尽的数据生成器至少可以生成“ steps_per_epoch * epochs”批次。您可能需要使用repeat（）函数？

0 个答案: