我有一個 keras 模型，它應該在(150, 150, 1)輸入和輸出長度為 8 的陣列時采用灰度影像。
這是我的模型代碼：
from tensorflow.python import keras
model = keras.Sequential([
keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation="relu", padding='same', input_shape=(150,150,1)),
keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'),
keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'),
keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'),
keras.layers.Flatten(),
keras.layers.Dense(8, activation="softmax")
])
當我嘗試使用該.predict()方法時，我收到此錯誤：
Traceback (most recent call last):
File "KerasCNN.py", line 152, in <module>
ga.run()
File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/pygad/pygad.py", line 1192, in run
self.last_generation_fitness = self.cal_pop_fitness()
File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/pygad/pygad.py", line 1159, in cal_pop_fitness
fitness = self.fitness_func(sol, sol_idx)
File "KerasCNN.py", line 112, in fitness
prediction = model.predict(g_img)
File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/models.py", line 966, in predict
return self.model.predict(x, batch_size=batch_size, verbose=verbose)
File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/training.py", line 1813, in predict
f, ins, batch_size=batch_size, verbose=verbose, steps=steps)
File "/home/User/Documents/Projects/2022/Keras_CNN/Trial1/env/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/training.py", line 1300, in _predict_loop
index_array = np.arange(num_samples)
TypeError: unsupported operand type(s) for /: 'Dimension' and 'int'
我之前運行了一個運行良好的 ANN（非 CNN）模型。當我進行一些研究時，我也可以找到有關此錯誤的任何資訊。
這是我用來進行預測的代碼：
img = get_image() # (150, 150, 3)
g_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # (150, 150, 1)
g_img = tf.expand_dim(g_img, axis=0)
g_img = tf.expand_dim(g_img, axis=-1) # (1, 150, 150, 1)
prediction = model.predict(g_img)
這是我的版本號：
張量流：1.5.0
Python：3.69
麻木：1.19.5
Ubuntu：18.04
讓我知道我是否可以提供任何其他資訊！謝謝！
回答
換成tf.expand_dim()固定的np.expand_dim()吧！
uj5u.com熱心網友回復：
這似乎在 TF 1.15 上運行得非常好：
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.python import keras
print(tf.__version__)
model = keras.Sequential([
keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation="relu", padding='same', input_shape=(150,150,1)),
keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'),
keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'),
keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'),
keras.layers.Flatten(),
keras.layers.Dense(8, activation="softmax")
])
# Create random image
img = np.zeros([150,150,3], dtype=np.uint8)
img[:,:,0] = np.ones([150,150])*64
img[:,:,1] = np.ones([150,150])*128
img[:,:,2] = np.ones([150,150])*192
g_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
g_img = np.expand_dims(g_img, axis=0)
g_img = np.expand_dims(g_img, axis=-1) # (1, 150, 150, 1)
prediction = model.predict(g_img)
print(prediction.shape)
1.15.2
(1, 8)
uj5u.com熱心網友回復：
在您撰寫的代碼中，輸入形狀是 (224,256,1) 所以將其更改為 (150,150,1) 試試這個：
from tensorflow.python import keras
model = keras.Sequential([
keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation="relu", padding='same', input_shape=(150,150,1)),
keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'),
做數學以獲得所需的輸出。
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