tensorflow 2.0ニューラルネットワークと全接続層のテンソル実戦


5.2テンソル実戦

  • テスト/検証(Test/Evaluation)
  • 精度(Accuracy)
  • 完全コード
  • 小結
  • テスト/検証(Test/Evaluation)

  • train/evaluation/test splitting
  • Stop at the best epoch
  • Use the best epoch model to p

  • 精度(Accuracy)

  • Pred: [Y, Y, Y, N, Y, N, N, Y, N, Y]
  • Label:[Y, N, Y, Y, N, Y, N, Y, N, Y]
  • Equal:[1, 0, 1, 0, 0, 0, 1, 1, 1, 1]
  • Acc: 6/10
  •     # test/evaluation
        # [w1, b1, w2, b2, w3, b3]
        total_correct, total_num = 0, 0
        for step, (x, y) in enumerate(test_db):
    
            # [b, 28, 28] => [b, 28*28]
            x = tf.reshape(x, [-1, 28 * 28])
    
            # [b, 784] => [b, 256] => [b, 128] => [b, 10]
            h1 = tf.nn.relu(x@w1 + b1)
            h2 = tf.nn.relu(h1@w2 + b2)
            out = h2@w3 + b3
    
            # out: [b, 10] ~ R
            # prob: [b, 10] ~ [0, 1]
            prob = tf.nn.softmax(out, axis=1)
            # [b, 10] => [b]
            # int64!!!
            pred = tf.argmax(prob, axis=1)
            pred = tf.cast(pred, dtype=tf.int32)
            # y: [b]
            # [b], int32
            # print(pred.dtype, y.dtype)
            correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
            correct = tf.reduce_sum(correct)
    
            total_correct += int(correct)
            total_num += x.shape[0]
    
        acc = total_correct / total_num
        print('test acc:', acc)
    

    完全なコード

    import tensorflow as tf
    from tensorflow import keras
    from tensorflow.keras import datasets
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    # x: [60k, 28, 28], [10, 28, 28]
    # y: [60k], [10k]
    (x, y), (x_test, y_test) = datasets.mnist.load_data()
    # # x: [0~255] => [0~1.]
    x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.
    y = tf.convert_to_tensor(y, dtype=tf.int32)
    x_test = tf.convert_to_tensor(x_test, dtype=tf.float32) / 255.
    y_test = tf.convert_to_tensor(y_test, dtype=tf.int32)
    
    print(x.shape, y.shape, x.dtype, y.dtype)
    print(tf.reduce_min(x), tf.reduce_max(x))
    print(tf.reduce_min(y), tf.reduce_max(y))
    
    
    train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128)
    test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(128)
    train_iter = iter(train_db)
    sample = next(train_iter)
    print('batch:', sample[0].shape, sample[1].shape)
    
    
    # [b, 784] => [b, 256] => [b, 128] => [b, 10]
    # [dim_in, dim_out], [dim_out]
    w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
    b1 = tf.Variable(tf.zeros([256]))
    w2 = tf.Variable(tf.random.truncated_normal([256, 128], stddev=0.1))
    b2 = tf.Variable(tf.zeros([128]))
    w3 = tf.Variable(tf.random.truncated_normal([128, 10], stddev=0.1))
    b3 = tf.Variable(tf.zeros([10]))
    
    lr = 1e-3
    
    for epoch in range(100):  # iterate db for 10
        for step, (x, y) in enumerate(train_db):  # for every batch
            # x:[128, 28, 28]
            # y: [128]
    
            # [b, 28, 28] => [b, 28*28]
            x = tf.reshape(x, [-1, 28 * 28])
    
            with tf.GradientTape() as tape:  # tf.Variable
                # x: [b, 28*28]
                # h1 = x@w1 + b1
                # [b, 784]@[784, 256] + [256] => [b, 256] + [256] => [b, 256] + [b, 256]
                h1 = x@w1 + tf.broadcast_to(b1, [x.shape[0], 256])
                h1 = tf.nn.relu(h1)
                # [b, 256] => [b, 128]
                h2 = h1@w2 + b2
                h2 = tf.nn.relu(h2)
                # [b, 128] => [b, 10]
                out = h2@w3 + b3
    
                # compute loss
                # out: [b, 10]
                # y: [b] => [b, 10]
                y_onehot = tf.one_hot(y, depth=10)
    
                # mse = mean(sum(y-out)^2)
                # [b, 10]
                loss = tf.square(y_onehot - out)
                # mean: scalar
                loss = tf.reduce_mean(loss)
    
            # compute gradients
            grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
            # print(grads)
            # w1 = w1 - lr * w1_grad
            w1.assign_sub(lr * grads[0])
            b1.assign_sub(lr * grads[1])
            w2.assign_sub(lr * grads[2])
            b2.assign_sub(lr * grads[3])
            w3.assign_sub(lr * grads[4])
            b3.assign_sub(lr * grads[5])
    
            if step % 100 == 0:
                print(epoch, step, 'loss:', float(loss))
    
        # test/evaluation
        # [w1, b1, w2, b2, w3, b3]
        total_correct, total_num = 0, 0
        for step, (x, y) in enumerate(test_db):
    
            # [b, 28, 28] => [b, 28*28]
            x = tf.reshape(x, [-1, 28 * 28])
    
            # [b, 784] => [b, 256] => [b, 128] => [b, 10]
            h1 = tf.nn.relu(x@w1 + b1)
            h2 = tf.nn.relu(h1@w2 + b2)
            out = h2@w3 + b3
    
            # out: [b, 10] ~ R
            # prob: [b, 10] ~ [0, 1]
            prob = tf.nn.softmax(out, axis=1)
            # [b, 10] => [b]
            # int64!!!
            pred = tf.argmax(prob, axis=1)
            pred = tf.cast(pred, dtype=tf.int32)
            # y: [b]
            # [b], int32
            # print(pred.dtype, y.dtype)
            correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
            correct = tf.reduce_sum(correct)
    
            total_correct += int(correct)
            total_num += x.shape[0]
    
        acc = total_correct / total_num
        print('test acc:', acc)
    

    小結


    回帰:
  • 方程式群
  • ノイズ
  • を導入する.
  • 導入勾配
  • Numpy実戦
  • 離散予測
  • を導入
    分類:
  • データ
  • を読み出す.
  • 構築モデル
  • 前方伝播実戦
  • 誤差計算
  • 勾配計算及び更新
  • テスト実戦