我實現了一個相對簡單的邏輯迴歸函數。我保存所有必要的變量,如重量,偏見,X,Y,等等,然後我跑訓練算法...如何在不同的輸入上使用訓練好的模型
# launch the graph
with tf.Session() as sess:
sess.run(init)
# training cycle
for epoch in range(FLAGS.training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples/FLAGS.batch_size)
# loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})
# compute average loss
avg_cost += c/total_batch
# display logs per epoch step
if (epoch + 1) % FLAGS.display_step == 0:
print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
save_path = saver.save(sess, "/tmp/model.ckpt")
該模型被保存,prediction
和訓練模型的accuracy
顯示...
# list of booleans to determine the correct predictions
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
print(correct_prediction.eval({x:mnist.test.images, y:mnist.test.labels}))
# calculate total accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
這是所有罰款和丹迪。但是,現在我希望能夠使用訓練好的模型來預測任何給定的圖像。例如,我想餵它的圖片說7
,看看它預測它是什麼。
我有另一個模塊恢復模型。首先我們加載變量...
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes
# set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# construct model
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
# minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(cost)
# initializing the variables
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
save.restore(sess, "/tmp/model.ckpt")
這很好。現在我想比較一個圖像和模型,並得到一個預測。在本例中,我從測試數據集mnist.test.images[0]
中獲取第一個圖像,並試圖將其與模型進行比較。
classification = sess.run(tf.argmax(pred, 1), feed_dict={x: mnist.test.images[0]})
print(classification)
我知道這是行不通的。我得到錯誤...
ValueError: Cannot feed value of shape (784,) for Tensor 'Placeholder:0', which has shape '(?, 784)'
我對創意不知所措。這個問題相當長,如果不可能直接得到答案,可以參考我可能採取的一些步驟。
我認爲你需要重塑你的圖像數組從(784,)到(1,784)。因爲784是單個特徵中的特徵,估計器需要形狀爲'(n_samples,n_features)'的數組 –