我有一个包含以下数据的 Numpy 数组，例如：
['13 .398249765480822 ''19 .324784598731966' '80 .98629514090669 '
'-3.703122956721927e-06' '80 .98629884402965 ''24 .008452881790028'
'679.6408224307851' '2498.8247399799975', 'fear']
另一个具有相同长度和不同数字的 Numpy 数组以及另一个“中性”标签。
事实是我正在使用 Github 的代码（Setosa）和其他文章来制作二进制分类器（恐惧或中立），但我收到以下错误，因为我不知道该怎么做，所以我考虑了所有数组中的数字而不是 Setosa 的代码，它在执行网格时只考虑两个。
## SVM con Tensorflow
sess = tf.Session()
x_vals = np.array([[x[0], x[1], x[2], x[3], x[4], x[5], x[6], x[7]] for x in matrix])
y_vals = np.array([1 if y[8] == 'fear' else -1 for y in matrix])
# Split the train data and testing data
train_indices = np.random.choice(len(x_vals), int(round(len(x_vals)*0.8)), replace=False)
test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))
x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]
class1_x = [x[0] for i, x in enumerate(x_vals_train) if y_vals_train[i] == 1]
class1_y = [x[1] for i, x in enumerate(x_vals_train) if y_vals_train[i] == 1]
class2_x = [x[0] for i, x in enumerate(x_vals_train) if y_vals_train[i] == -1]
class2_y = [x[1] for i, x in enumerate(x_vals_train) if y_vals_train[i] == -1]
# Declare batch size
batch_size = 150
# Initialize placeholders
x_data = tf.placeholder(shape=[None, 8], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)
prediction_grid = tf.placeholder(shape=[None, 8], dtype=tf.float32)
# Create variables for svm
b = tf.Variable(tf.random_normal(shape=[1, batch_size]))
# Gaussian (RBF) kernel
gamma = tf.constant(-10.0)
sq_dists = tf.multiply(2., tf.matmul(x_data, tf.transpose(x_data)))
my_kernel = tf.exp(tf.multiply(gamma, tf.abs(sq_dists)))
# Compute SVM Model
first_term = tf.reduce_sum(b)
b_vec_cross = tf.matmul(tf.transpose(b), b)
y_target_cross = tf.matmul(y_target, tf.transpose(y_target))
second_term = tf.reduce_sum(tf.multiply(my_kernel, tf.multiply(b_vec_cross, y_target_cross)))
loss = tf.negative(tf.subtract(first_term, second_term))
# Gaussian (RBF) prediction kernel
rA = tf.reshape(tf.reduce_sum(tf.square(x_data), 1), [-1, 1])
rB = tf.reshape(tf.reduce_sum(tf.square(prediction_grid), 1), [-1, 1])
pred_sq_dist = tf.add(tf.subtract(rA, tf.multiply(2., tf.matmul(x_data, tf.transpose(prediction_grid)))), tf.transpose(rB))
pred_kernel = tf.exp(tf.multiply(gamma, tf.abs(pred_sq_dist)))
prediction_output = tf.matmul(tf.multiply(tf.transpose(y_target), b), pred_kernel)
prediction = tf.sign(prediction_output - tf.reduce_mean(prediction_output))
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.squeeze(prediction), tf.squeeze(y_target)), tf.float32))
# Declare optimizer
my_opt = tf.train.GradientDescentOptimizer(0.01)
train_step = my_opt.minimize(loss)
# Initialize variables
init = tf.global_variables_initializer()
sess.run(init)
# Training loop
loss_vec = []
batch_accuracy = []
for i in range(300):
rand_index = np.random.choice(len(x_vals), size=batch_size)
rand_x = x_vals[rand_index]
rand_y = np.transpose([y_vals[rand_index]])
sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
loss_vec.append(temp_loss)
acc_temp = sess.run(accuracy, feed_dict={x_data: rand_x,
y_target: rand_y,
prediction_grid: rand_x})
batch_accuracy.append(acc_temp)
if (i + 1) % 75 == 0:
print('Step #' + str(i + 1))
print('Loss = ' + str(temp_loss))
# Create a mesh to plot points in
x_vals = x_vals.astype(np.float)
x_min, x_max = x_vals[:, 0].min() - 1, x_vals[:, 0].max() + 1
y_min, y_max = x_vals[:, 1].min() - 1, x_vals[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
np.arange(y_min, y_max, 0.02))
grid_points = np.c_[xx.ravel(), yy.ravel()]
[grid_predictions] = sess.run(prediction, feed_dict={x_data: x_vals,
y_target: np.transpose([y_vals]),
prediction_grid: grid_points})
grid_predictions = grid_predictions.reshape(xx.shape)
# Plot points and grid
plt.contourf(xx, yy, grid_predictions, cmap=plt.cm.Paired, alpha=0.8)
plt.plot(class1_x, class1_y, 'ro', label='I. setosa')
plt.plot(class2_x, class2_y, 'kx', label='Non setosa')
plt.title('Gaussian SVM Results on Iris Data')
plt.xlabel('Petal Length')
plt.ylabel('Sepal Width')
plt.legend(loc='lower right')
plt.ylim([-0.5, 3.0])
plt.xlim([3.5, 8.5])
plt.show()
# Plot batch accuracy
plt.plot(batch_accuracy, 'k-', label='Accuracy')
plt.title('Batch Accuracy')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()
# Plot loss over time
plt.plot(loss_vec, 'k-')
plt.title('Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Loss')
plt.show()
得到的错误是：
File "test.py", line 154, in <module>
prediction_grid: grid_points})
ValueError: Cannot feed value of shape (30119320, 2) for Tensor u'Placeholder_2:0', which has shape '(?, 8)'
我知道它们的形状不同，但我不知道如何改变它或做什么，因为我需要制作一个具有 8 个特征和两个类“中性”和“恐惧”的分类器。
原始代码在这里。