1
안녕하세요, 나는 tensorflow에 꽤 새로 왔습니다. 나는 기본적으로 0/1로 분류되는 분류 모델을 구축 중입니다. 출력 1의 확률을 예측할 수있는 방법이 있습니까? 여기에서 predict_proba를 사용할 수 있습니까? 그 널리 tflearn.dnn에서 사용하지만 내 경우에는 그것을 할 어떤 참조를 찾을 수 없습니다. 여기classfier tensorflow에서 확률 예측
def main():
train_x,test_x,train_y,test_y = load_csv_data()
x_size = train_x.shape[1]
y_size = train_y.shape[1]
print(x_size)
print(y_size)
# variables
X = tf.placeholder("float", shape=[None, x_size])
y = tf.placeholder("float", shape=[None, y_size])
weights_1 = initialize_weights((x_size, h_size))
weights_2 = initialize_weights((h_size, y_size))
# Forward propagation
y_pred = forward_propagation(X, weights_1, weights_2)
predict = tf.argmax(y_pred, dimension=1)
# Backward propagation
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_pred))
updates_sgd = tf.train.GradientDescentOptimizer(sgd_step).minimize(cost)
# Start tensorflow session
with tf.Session() as sess:
init = tf.global_variables_initializer()
steps = 1
sess.run(init)
x = np.arange(steps)
test_acc = []
train_acc = []
print("Step, train accuracy, test accuracy")
for step in range(steps):
# Train with each example
batch_size = len(train_x)
avg_cost = 0
print(batch_size)
for i in range(len(train_x)):
_, c = sess.run([updates_sgd,cost], feed_dict={X: train_x[i: i + 1], y: train_y[i: i + 1]})
print(c)
avg_cost += c/batch_size
train_accuracy = np.mean(np.argmax(train_y, axis=1) ==
sess.run(predict, feed_dict={X: train_x, y: train_y}))
test_accuracy = np.mean(np.argmax(test_y, axis=1) ==
sess.run(predict, feed_dict={X: test_x, y: test_y}))
print(avg_cost)
print("%d, %.2f%%, %.2f%%"
% (step + 1, 100. * train_accuracy, 100. * test_accuracy))
test_acc.append(100. * test_accuracy)
train_acc.append(100. * train_accuracy)
predict = tf.argmax(y_pred,1)
test_data = load_test_data()
print(test_data)
pred = predict.eval(feed_dict={X:test_data})
print(pred)
for x in range(0,100):
print(pred[x])
print(np.unique(pred))
main()
쿨! 감사 :) – sumit