tensorflow - これらのリストは、Keras チューナーの検索機能で受け入れられるようにするには、どのような形式にする必要がありますか?

Question

このコードは、ニューラル ネットワークが学習およびテストするための一連のテストおよびトレーニング ギター jpg 画像を読み込みます。

 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)

この次のコードは、前のコードで保存された pickle ファイルを取り込み、Keras チューナーの検索機能を使用して、さまざまな量の変換層、層サイズなどでニューラル ネットワークのさまざまなバリアントを実行することになっているため、最も効率的なバージョンを選択できます。しかし、実行すると、このエラーがスローされます:

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

すべての変数の形状は次のとおりです。

(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)

このエラーを解決するにはどうすればよいですか? 私にはマトリックスの書式設定の問題のように思えますが、このような問題に対処した経験はほとんどありません。