ValueError：层顺序的输入 0 与层不兼容：：预期 min_ndim=4，发现 ndim=3。收到的完整形状：（10、300、3）

Question

我正在尝试为学校项目编写入门代码，这是我第一次使用机器学习或类似的东西，因此非常感谢任何帮助。我尝试从一本书开始，并且完全按照代码进行操作，并且几乎理解了所有内容。但是我还不太明白如何调试我的程序，并且我已经检查了堆栈溢出是否存在这个问题，但我仍然不太明白如何解决这个问题。

import tensorflow as tf
import numpy as np
from google.colab import files
from keras.preprocessing import image
import random
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import zipfile
import os

!wget --no-check-certificate \
    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \
    -O /tmp/horse-or-human.zip

local_zip = '/tmp/horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp/horse-or-human')
zip_ref.close()

train_horse_dir = os.path.join('/tmp/horse-or-human/horses')
train_human_dir = os.path.join('/tmp/horse-or-human/humans')

train_horse_names = os.listdir(train_horse_dir)
print(train_horse_names[:10])

train_human_names = os.listdir(train_human_dir)
print(train_human_names[:10])

print('total training horse images:', len(os.listdir(train_horse_dir)))
print('total training human images:', len(os.listdir(train_human_dir)))

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(loss='binary_crossentropy',
              optimizer=RMSprop(lr=0.001),
              metrics=['accuracy'])

train_datagen = ImageDataGenerator(rescale=1/255)

train_generator = train_datagen.flow_from_directory(
        '/tmp/horse-or-human/',  
        target_size=(300, 300),  
        batch_size=128,
        class_mode='binary')

history = model.fit(
      train_generator,
      steps_per_epoch=8,   
      epochs=15,
      verbose=1)

model.summary() 

uploaded = files.upload()

for fn in uploaded.keys():
  
  path = '/content/' + fn
  img = image.load_img(path, target_size=(300, 300))
  x = image.img_to_array(img)
  y = np.expand_dims(x, axis=0)

  image_tensor = np.vstack([x])
  classes = model.predict(image_tensor, batch_size=10)
  print(classes)
  print(classes[0])
  if classes[0]>0.5:
    print(fn + " is a human")
  else:
    print(fn + " is a horse")

然后我得到错误代码

ValueError: in user code:

    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1569 predict_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1559 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1552 run_step  **
        outputs = model.predict_step(data)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1525 predict_step
        return self(x, training=False)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1013 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py:235 assert_input_compatibility
        str(tuple(shape)))

    ValueError: Input 0 of layer sequential_2 is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (10, 300, 3)

任何帮助将不胜感激:)

Answer 1

您将批次维度分配给y 变量时出错。

像这样改变这一行：

#y = np.expand_dims(x, axis=0)
x = np.expand_dims(x, axis=0)