xentropy_sigmoid_y_out=sess.run(xentropy_sigmoid_y_vals)#Weighted (softmax) cross entropy loss#L = -actual * (log(pred)) * weights - (1-actual)(log(1-pred))#or#L = (1 - pred) * actual + (1 + (weights - 1) * pred) * log(1 + exp(-actual))weight = tf.constant(0.5)...
loss=None, # 损失函数值。常见的有二元交叉熵(BinaryCrossentropy),绝对交叉熵(CategoricalCrossentropy),稀疏分类交叉熵(SparseCategoricalCrossentropy)等 metrics=None, # 典型的是metrics=['accuracy'] loss_weights=None, weighted_metrics=None, run_eagerly=None, steps_per_execution=None, # 指定每一次训练所...
xentropy_sigmoid_y_vals = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_val_input, labels=target_input) xentropy_sigmoid_y_out = sess.run(xentropy_sigmoid_y_vals) # Weighted (softmax) cross entropy loss # L = -actual * (log(pred)) * weights - (1-actual)(log(1-pred)) # or...
base_learning_rate=0.0001# 编译模型 model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate),loss='binary_crossentropy',metrics=['accuracy'])# 各部分数据数量 num_train,num_val,num_test=(metadata.splits['train'].num_examples*weight/10forweightinSPLIT_WEIGHTS)# 迭代次数 initial...
The value of the weighted cross entropy. """flat_labels=tf.cast(tf.reshape(labels,[-1,n_classes]),dtype=tf.float32)flat_logits=tf.cast(tf.reshape(logits,[-1,n_classes]),dtype=tf.float32)weight_cross_entropy=-tf.reduce_sum(tf.multiply(flat_labels*tf.math.log(flat_logits),weights),...
1.模型相关参数设置。因为为二分类任务(猫和狗),因此loss函数为binary_crossentropy。 base_learning_rate = 0.0001 model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate), loss='binary_crossentropy', metrics=['accuracy'])
2.7 binary_crossentropy 二进制交叉熵 defbinary_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0): y_pred=ops.convert_to_tensor_v2_with_dispatch(y_pred) y_true=math_ops.cast(y_true, y_pred.dtype) label_smoothing=ops.convert_to_tensor_v2_with_dispatch( ...
binary_crossentropy(y, prop), name="cvr_loss") ctr_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=labels['ctr'], logits=ctr_logits), name="ctr_loss") loss = tf.add(ctr_loss, cvr_loss, name="ctcvr_loss") ctr_accuracy = tf.metrics.accuracy(labels=labels['...
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"]) 训练: H = model.fit( trainAug.flow(trainX, trainY, batch_size=BS), steps_per_epoch=len(trainX) // BS, validation_data=(testX, testY), validation_steps=...
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits = tf.nn.l2_normalize(logits,-1), labels = corr) # Add regularization losses, weighted by weight_decay. total_loss = tf.reduce_mean(loss) + weight_decay * tf.add_n( tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) ...