我正在尝试在 Iris 数据集上运行一个标准的神经网络。标签是一个单列，可以根据物种类型取值0、1、2。我将特征转置到 x 轴上，将样本转置到 y 轴上。
需要关注的领域：成本函数 – 大家似乎都在使用预构建的成本函数，但由于我的数据不是独热编码的，我使用的是标准损失函数。优化器 – 我把它当成一个黑盒子使用，不确定是否能正确更新成本函数。
提前感谢您的帮助。
import tensorflow as tfimport numpy as npimport pandas as pdimport tensorflow as tfdef create_layer(previous_layer, weight, bias, activation_function=None): z = tf.add(tf.matmul(weight, previous_layer), bias) if activation_function is None: return z a = activation_function(z) return adef cost_compute(prediction, correct_values): return tf.nn.softmax_cross_entropy_with_logits(logits = prediction, labels = correct_values)input_features = 4n_hidden_units1 = 10n_hidden_units2 = 14n_hidden_units3 = 12n_hidden_units4 = 1rate = .000001weights = dict( w1=tf.Variable(tf.random_normal([n_hidden_units1, input_features])), w2=tf.Variable(tf.random_normal([n_hidden_units2, n_hidden_units1])), w3=tf.Variable(tf.random_normal([n_hidden_units3, n_hidden_units2])), w4=tf.Variable(tf.random_normal([n_hidden_units4, n_hidden_units3])) )biases = dict( b1=tf.Variable(tf.zeros([n_hidden_units1, 1])), b2=tf.Variable(tf.zeros([n_hidden_units2, 1])), b3=tf.Variable(tf.zeros([n_hidden_units3, 1])), b4=tf.Variable(tf.zeros([n_hidden_units4, 1])) )train = pd.read_csv("/Users/yazen/Desktop/datasets/iris_training.csv")test = pd.read_csv("/Users/yazen/Desktop/datasets/iris_test.csv")train.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'species']test.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'species']train_labels = np.expand_dims(train['species'].as_matrix(), 1)test_labels = np.expand_dims(test['species'].as_matrix(), 1)train_features = train.drop('species', axis=1)test_features = test.drop('species', axis=1)test_labels = test_labels.transpose()train_labels = train_labels.transpose()test_features = test_features.transpose()train_features = train_features.transpose()x = tf.placeholder("float32", [4, None], name="asdfadsf")y = tf.placeholder("float32", [1, None], name="asdfasdf2")layer = create_layer(x, weights['w1'], biases['b1'], tf.nn.relu)layer = create_layer(layer, weights['w2'], biases['b2'], tf.nn.relu)layer = create_layer(layer, weights['w3'], biases['b3'], tf.nn.relu)Z4 = create_layer(layer, weights['w4'], biases['b4'])cost = cost_compute(Z4, y)with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for iteration in range(1,50): optimizer = tf.train.GradientDescentOptimizer(learning_rate=rate).minimize(cost) _, c = sess.run([optimizer, cost], feed_dict={x: train_features, y: train_labels}) print("Iteration " + str(iteration) + " cost: " + str(c)) prediction = tf.equal(Z4, y) accuracy = tf.reduce_mean(tf.cast(prediction, "float")) print(sess.run(Z4, feed_dict={x: train_features, y: train_labels})) print(accuracy.eval({x: train_features, y: train_labels}))回答：
由于您面对的是一个分类问题，您需要将标签转换为独热编码形式。您可以使用 tf.one_hot 来实现这一点。此外，您还可以在成本函数上应用 tf.reduce_mean，如下面的示例所示（示例来自 这里）。此外，您的学习率对我来说似乎太小了。
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.matmul(x, W) + b # Define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.InteractiveSession() tf.global_variables_initializer().run() # Train for _ in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # Test trained model correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))