Part3:predict任务部分。如果任务目的是predict,那么可以直接通过网络结构计算pred,不需要其他操作。设置好export_outputs,并以tf.estimator.EstimatorSpec形式返回即可。 Part4:eval任务部分。如果是eval任务,除了网络结构以外还需要计算此时的损失、正确率等指标,所以对于loss的定义要放在这一部分。同时设置好评价指标eval...
28),cmap='gray_r')plt.axis('off')plt.title(title)defbatchDraw(batch_size):images,labels=mnist.train.next_batch(batch_size)image_number=images.shape[0]row_number=math.ceil(image_number**0.5)column_number=row_number
b= tf.Variable(tf.zeros([10]))#LOGISTIC REGRESSION MODEL #get the predict numberactv = tf.nn.softmax(tf.matmul(x, W) +b)#COST FUNCTIONcost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1))#OPTIMIZERlearning_rate = 0.01optm= tf.train.GradientDescentOptimizer(l...
iterator = dataset.repeat().batch(batch_size).make_initializable_iterator() data_batch = iterator.get_next( 现在我们定义了一个数据流图,为了获得一批新图像和新标签,我们在会话中运行data_batch。 但是还未完成。虽然数据流图创建好了,但图像还没有被完全传递进来。为此,迭代器需要在会话中初始化。 sess ...
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("-...
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_idx_p, max_idx_l) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) ...
# 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 ...
defmodel_fn(features,labels,mode,params):# Args:## features: This is the x-arg from the input_fn.# labels: This is the y-arg from the input_fn,# see e.g. train_input_fn for these two.# mode: Either TRAIN, EVAL, or PREDICT# params: User-defined hyper-parameters, e.g. learnin...
--learning_rate=0.0005 --optimizer=Adam --num_epochs=1 --batch_size=256 --field_size=39 --feature_size=117581 --deep_layers=400,400,400 --dropout=0.5,0.5,0.5 --log_steps=1000 --num_threads=8 --model_dir=./model_ckpt/criteo/DeepFM/ --data_dir=../../data/criteo/#predict...
#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 ...