在我适合我的机器学习之后,我正在尝试预测一个新的机器学习。名为demo1.jpg的图像
我期望的是在我的库中添加新特性:
我的详细信息:
RTX 2080
Tensorflow 1.13.1
Cuda 10.0
我正在使用tf.keras,并且收到以下错误:
ValueError:检查输入时出错:要求conv2d_input具有4维,但得到形状为(1,1)的数组
我的完整代码:
import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
import tensorflow as tf
import numpy as np
import pickle
import cv2
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow import keras
IMG_SIZE = 50
def prepare(file):
img_array = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
predictdata = tf.reshape(new_array, (1, 50, 50))
predictdata = np.expand_dims(predictdata, -1)
return predictdata
pickle_ind = open("x.pickle", "rb")
x = pickle.load(pickle_ind)
x = np.array(x, dtype=float)
x = np.expand_dims(x, -1)
pickle_ind = open("y.pickle", "rb")
y = pickle.load(pickle_ind)
n_batch = len(x)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(50, 50, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(1, activation='softmax'))
model.summary()
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(x, y, epochs=1, batch_size=n_batch)
prediction = model.predict([prepare('demo1.jpg')], batch_size=n_batch, steps=1, verbose=1)
print(prediction)
发布于 2019-06-11 03:22:38
执行以下更改:
def prepare(file):
img_array = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
return np.expand_dims(cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)), -1)
model.fit(x, y, epochs=1, batch_size=n_batch)
model.predict(np.array([prepare("demo1.jpg")]), batch_size=n_batch, steps=1, verbose=1)
问题: tf.reshape
返回张量,而不是numpy数组。然后,expand_dims
添加一个维数并返回一个单元素np数组(该元素是张量)。
而是以3D np数组的形式返回图像,然后创建一批用于预测的图像。
https://stackoverflow.com/questions/56531748
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