现在,通过解码以下第一行中的所有测试集,然后循环遍历一个固定数字(number_of_items)并显示它们,来对测试集中的一些噪点图像进行去噪。 请注意,在显示每个图像(im)之前,需要对其进行重塑: 代码语言:javascript 复制 decoded_images = autoencoder.predict(test_noisy_x) number_of_items = 10 plt.figure(figsize=...
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]) max_idx_p = tf.argmax(predict, 2) max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2) correct_pred = tf.equal(max_id...
预测之后250个时间点,结果保存在predictions['mean']中: (predictions,) = tuple(ar.predict( input_fn=tf.contrib.timeseries.predict_continuation_input_fn( evaluation, steps=250))) 画出原始、拟合、预测: plt.figure(figsize=(15,5))plt.plot(data['times'].reshape(-1),data['values'].reshape(-1...
# using a categorical distribution to predict the word returned by the model predictions = predictions / temperature predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy() # We pass the predicted word as the next input to the model # along with the previous hidden ...
【本文使用的是TensorFlow1.x,如需TensorFlow2.x的内容参见我的“TensorFlow2实战”笔记】 一、问题描述 手写数字识别问题是一种分类问题,即输...
(x,weight) + bias# 3、模型参数计算withtf.variable_scope("model_soft_corss"):# 计算交叉熵损失softmax = tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_predict)# 计算损失平均值loss = tf.reduce_mean(softmax)# 4、梯度下降(反向传播算法)优化模型withtf.variable_scope("model_...
#y_bias = FM_B * tf.ones_like(labels, dtype=tf.float32) # None * 1 warning;这里不能用label,否则调用predict/export函数会出错,train/evaluate正常;初步判断estimator做了优化,用不到label时不传 y_bias = FM_B * tf.ones_like(y_d, dtype=tf.float32) # None * 1 ...
predictions={"prob":pred}export_outputs={tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:tf.estimator.export.PredictOutput(predictions)}# Provide an estimator specfor`ModeKeys.PREDICT`ifmode==tf.estimator.ModeKeys.PREDICT:returntf.estimator.EstimatorSpec(mode=mode,predictions=predictions...
import tensorflow as tf with tf.device('/cpu:0'): for i in range(10): t = np.random.randint(len(x_test) ) x1= x_test[t] x1 = x1.reshape(1,28,28,1) p = model.predict(x1) y1 = y_test[t] print("---") print(labels[np.argmax([p])]) print(labels[y1]) print("-...
predict = [] for i in range(0, np.shape(train_x)[0]): next_seq = sess.run(pred, feed_dict={X: [train_x[i]]}) predict.append(next_seq[-1]) plt.figure() plt.plot(list(range(len(data))), data, color='b') plt.plot(list(range(time_step + 1, np.shape(train_x)[0] ...