Question

[EDITED] 大家好，我正在尝试学习tensorflow来构建一个特定的应用程序，所以我试图重新创建我在互联网上找到的这个例子（https://medium.com/@curiousily/human-activity-recognition-using-lstms-on-android-tensorflow-for-hackers-part-vi-492da5adef64）。

构建模型：

N_CLASSES = 6
N_HIDDEN_UNITS = 64
def create_LSTM_model(inputs):
    W = {
        'hidden': tf.Variable(tf.random_normal([N_FEATURES, N_HIDDEN_UNITS])),
        'output': tf.Variable(tf.random_normal([N_HIDDEN_UNITS, N_CLASSES]))
    }
    biases = {
        'hidden': tf.Variable(tf.random_normal([N_HIDDEN_UNITS], mean=1.0)),
        'output': tf.Variable(tf.random_normal([N_CLASSES]))
    }

    X = tf.transpose(inputs, [1, 0, 2])
    X = tf.reshape(X, [-1, N_FEATURES])
    hidden = tf.nn.relu(tf.matmul(X, W['hidden']) + biases['hidden'])
    hidden = tf.split(hidden, N_TIME_STEPS, 0)

    # Stack 2 LSTM layers
    lstm_layers = [tf.contrib.rnn.BasicLSTMCell(N_HIDDEN_UNITS, forget_bias=1.0) for _ in range(2)]
    lstm_layers = tf.contrib.rnn.MultiRNNCell(lstm_layers)

    outputs, _ = tf.contrib.rnn.static_rnn(lstm_layers, hidden, dtype=tf.float32)

    # Get output for the last time step
    lstm_last_output = outputs[-1]

    return tf.matmul(lstm_last_output, W['output']) + biases['output']

为我们的模型创建占位符：

tf.reset_default_graph()    
X = tf.placeholder(tf.float32, [None, N_TIME_STEPS, N_FEATURES], name="input")
Y = tf.placeholder(tf.float32, [None, N_CLASSES])

pred_Y = create_LSTM_model(X)

pred_softmax = tf.nn.softmax(pred_Y, name="y_")
L2_LOSS = 0.0015

l2 = L2_LOSS * \
    sum(tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables())

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred_Y, labels = Y)) + l2
LEARNING_RATE = 0.0025

optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE).minimize(loss)

correct_pred = tf.equal(tf.argmax(pred_softmax, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, dtype=tf.float32))

问题在于模型培训：

N_EPOCHS = 50
BATCH_SIZE = 1024
saver = tf.train.Saver()

history = dict(train_loss=[], 
                     train_acc=[], 
                     test_loss=[], 
                     test_acc=[])

sess=tf.InteractiveSession()
sess.run(tf.global_variables_initializer())

train_count = len(X_train)

for i in range(1, N_EPOCHS + 1):
    for start, end in zip(range(0, train_count, BATCH_SIZE),
                          range(BATCH_SIZE, train_count + 1,BATCH_SIZE)):
        sess.run(optimizer, feed_dict={X: X_train[start:end],
                                       Y: y_train[start:end]})

    _, acc_train, loss_train = sess.run([pred_softmax, accuracy, loss], feed_dict={
                                            X: X_train, Y: y_train})

    _, acc_test, loss_test = sess.run([pred_softmax, accuracy, loss], feed_dict={
                                            X: X_test, Y: y_test})

    history['train_loss'].append(loss_train)
    history['train_acc'].append(acc_train)
    history['test_loss'].append(loss_test)
    history['test_acc'].append(acc_test)

    if i != 1 and i % 10 != 0:
        continue

    print(f'epoch: {i} test accuracy: {acc_test} loss: {loss_test}')

predictions, acc_final, loss_final = sess.run([pred_softmax, accuracy, loss], feed_dict={X: X_test, Y: y_test})

print()
print(f'final results: accuracy: {acc_final} loss: {loss_final}')

上面的链接中有更多代码。这是输出：

epoch: 1 test accuracy: 0.29729729890823364 loss: 3.0542454719543457
epoch: 10 test accuracy: 0.29729729890823364 loss: 3.0542454719543457
epoch: 20 test accuracy: 0.29729729890823364 loss: 3.0542454719543457
epoch: 30 test accuracy: 0.29729729890823364 loss: 3.0542454719543457
epoch: 40 test accuracy: 0.29729729890823364 loss: 3.0542454719543457
epoch: 50 test accuracy: 0.29729729890823364 loss: 3.0542454719543457

我真的无法理解为什么在每个时代都能获得相同的输出精度和损失。

谢谢大家，我对ML和tensowflow很新，我不太了解它。

张量损失和准确度不会在时代之间发生变化

0 个答案: