在 GPU 上运行 TensorFlow 教程 (MNIST)

- Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. alled properly if you would like to use GPU. Follow the guide 
- Skipping registering GPU devices...
- 2022-07-25 17:44:34.995926: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2 wing CPU instructions in performance-critical operations:  AVX
- To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
- <TensorSliceDataset element_spec=TensorSpec(shape=(), dtype=tf.int32, name=None)>

- W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found

import os; os.add_dll_directory(os.path.join(os.environ['CUDA_PATH'], 'bin'));

- W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found

import os; os.add_dll_directory(os.path.join(os.environ['CUDA_PATH'], 'bin')); # GPUを使用するためのDLL読み込み設定
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test_org, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test_org / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
])

predictions = model(x_train[:1]).numpy()
tf.nn.softmax(predictions).numpy()
print(predictions)

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# print(loss_fn(y_train[:1], predictions).numpy())

model.compile(optimizer='adam',
                loss=loss_fn,
                metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test, verbose=2)

probability_model = tf.keras.Sequential([
    model,
    tf.keras.layers.Softmax()
])
target = 0
while target >= 0:
    target = int(input("テスト画像のindexを入力してね"))
    if target < 0:
        next
    plt.imshow(x_test_org[target],
                cmap=plt.cm.binary)
    plt.show()
    testdata = x_test[target:target+1]
    ans = probability_model(testdata)
    ans_idx = np.argmax(ans[0])
    print("この文字は" + str(ans_idx) + "だと思う")
    print(y_test[target:target+1])