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此代码读取一组测试和训练吉他 jpg 图像，供神经网络学习和测试。

 import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
import random

DATADIR = "C:/Users/TheKid/Data/DataMiningProject/DataSet"
CATEGORIES = ["Fender_Jazzmaster", "Gibson_ES"]
CATEGORIES2 = ["Test"]

for category in CATEGORIES:
    path = os.path.join(DATADIR,category)
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)
    
IMG_SIZE = 70

new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))

training_data = []
def create_training_data():
        for category in CATEGORIES:
            path = os.path.join(DATADIR,category)
            class_num = CATEGORIES.index(category)
            for img in os.listdir(path):
                img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)
                new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
                training_data.append([new_array,class_num])

create_training_data()

print(len(training_data))
random.shuffle(training_data)

X = []
y = []

for features, label in training_data:
    X.append(features)
    y.append(label)

X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)              

for category in CATEGORIES2:
    path2 = os.path.join(DATADIR,category)
    for img in os.listdir(path2):
        img_array2 = cv2.imread(os.path.join(path2,img),cv2.IMREAD_GRAYSCALE)
    
IMG_SIZE = 70

new_array2 = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))

testing_data = []
def create_testing_data():
        for category in CATEGORIES2:
            path2 = os.path.join(DATADIR,category)
            class_num2 = CATEGORIES2.index(category)
            for img in os.listdir(path2):
                img_array2 = cv2.imread(os.path.join(path2,img),cv2.IMREAD_GRAYSCALE)
                new_array2 = cv2.resize(img_array2,(IMG_SIZE,IMG_SIZE))
                testing_data.append([new_array2,class_num2])                
                
            

create_testing_data()

print(len(testing_data))
random.shuffle(testing_data)

X2 = []
y2 = []

for features, label in testing_data:
    X2.append(features)
    y2.append(label)

X2 = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1) 

import pickle

pickle_out = open("X.pickle" , "wb")
pickle.dump(X, pickle_out)
pickle_out.close()

pickle_out = open("y.pickle" , "wb")
pickle.dump(y, pickle_out)
pickle_out.close()

pickle_in = open("X.pickle", "rb")
X = pickle.load(pickle_in)

pickle_out = open("X2.pickle" , "wb")
pickle.dump(X2, pickle_out)
pickle_out.close()

pickle_out = open("y2.pickle" , "wb")
pickle.dump(y2, pickle_out)
pickle_out.close()

pickle_in = open("X2.pickle", "rb")
X = pickle.load(pickle_in)

下一段代码采用之前代码中保存的 pickle 文件，并且应该使用 Keras 调谐器搜索功能来运行具有不同数量的卷积层、层大小等的神经网络的不同变体，因此我可以选择最有效的版本。但是运行时会抛出这个错误：

 ValueError: Data cardinality is ambiguous:
  x sizes: 1312
  y sizes: 12
Please provide data which shares the same first dimension.

所有变量的形状为：

(x_train = (1312, 70, 70, 1)

y_train =(1312,)

x_test = (1312, 70, 70, 1)

y_test =(12,)

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
import numpy as np
import time
import pickle
import matplotlib.pyplot as plt
from tensorflow import keras
from kerastuner.tuners import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters




pickle_in = open("X.pickle","rb")
x_train = pickle.load(pickle_in)

pickle_in = open("y.pickle","rb")
y_train = pickle.load(pickle_in)

pickle_in = open("X2.pickle","rb")
x_test = pickle.load(pickle_in)

pickle_in = open("y2.pickle","rb")
y_test = pickle.load(pickle_in)



x_train=np.array(x_train/255.0)
y_train=np.array(y_train)

x_test=np.array(x_test/255.0)
y_test=np.array(y_test)


LOG_DIR = f"{int(time.time())}"

def build_model(hp):
    model = keras.models.Sequential()

    model.add(Conv2D(hp.Int("input_units",32, 256, 32 ), (3, 3), input_shape=x_train.shape[1:]))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    for i in range(hp.Int("n_layers", 1, 4)):
        model.add(Conv2D(hp.Int(f"conv-{i}_units",32, 256, 32 ), (3, 3)))
        model.add(Activation('relu'))
    

    model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

    model.add(Dense(10))
    model.add(Activation("softmax"))

    model.compile(optimizer="adam",
                  loss="binary_crossentropy",
                  metrics=["accuracy"])

    return model

tuner = RandomSearch(
        build_model,
        objective = "val_accuracy",
        max_trials = 1,
        executions_per_trial = 1,
        directory = LOG_DIR)



tuner.search(x=x_train,
            y=y_train,
            epochs=1,
            batch_size=64,
            validation_data=(x_test,y_test))

with open(f"tuner_{int(time.time())}.pkl", "wb") as f:
    pickle.dump(tuner, f)

tuner = pickle.load(open(""))

print(tuner.get_best_hyperparameters()[0].values)

我将如何解决这个错误？对我来说，这似乎是一个矩阵格式问题，但我在处理此类问题方面经验不足。

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1 回答 1

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正如数据 (x_test和y_test) 的错误消息和形状清楚地表明的那样，您有1312行 inx_test和12行 in y_test。您正在将此数据提供给validation_data=(x_test,y_test).

x_test请为和输入相同的维度或相同的数据行，y_test这validation_data应该可以解决您的错误。

于 2020-11-02T05:02:44.060 回答