SparseTopKCategoricalAccuracy(5)]) return(model) model = create_model() model.summary() model = compile_model(model) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 Model: "sequential" ___________________________________
源码在tensorflow的model项目中 models-master\research\slim\nets\inception_resnet_v2.py。 训练方式:train集合中每张图片在训练是都经过随机数据增强,以保证每张训练图片都不同,每10个epoch,用验证集进行一次 accuracy验证并导出对应pb文件,训练脚本参考 models-master\research\slim\train_image_classifier.py 进行对...
保存和加载包含自定义组件的模型 当加载包含自定义对象的模型时,需要将名称映射到对象。不幸的是,当你保存模型时,阈值不会被保存,这意味着在加载模型时必须指定阈值。你可以通过创建keras.losses.Loss类的子类,然后实现其get_config()方法来解决此问题 Keras API当前仅指定如何使用子类定义层、模型、回调和正则化。如...
model=create_model()model.fit(train_images,train_labels,epochs=5)# 以SavedModel格式保存整个模型 model.save("saved_model/my_model")new_model=tf.keras.models.load_model("saved_model/my_model")# 看到模型的结构 new_model.summary()# 评估模型 loss,acc=new_model.evaluate(test_images,test_labels...
model = tf.keras.Sequential([tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(10, activation='softmax')]) return model model = create_model() x_train = np.random.rand(1000, 10) y_train = np....
以识别手语数字为例,创建卷积网络模型 导入相应模块 import math import numpy as np import h5py import matplotlib.pyplot as plt import scipy from PIL import Image from scipy import ndimage import tensorflow as tf from tensorflow.python.framework import ops ...
model = create_model()# 创建一个模型loss_fn = keras.losses.MeanSquaredError()# 定义损失函数optimizer = keras.optimizers.SGD()# 定义优化器epochs =1000# 训练次数forepochinrange(epochs):withtf.GradientTape()astape: y_pred = model(x_train, training=True)# 前向传播,注意不要忘了training=True...
1function createModel() 2{ 3var model = tf.sequential() 4model.add(tf.layers.dense({units:8, inputShape:2,activation: 'tanh'})) 5model.add(tf.layers.dense({units:1, activation: 'sigmoid'})) 6model.compile({optimizer: 'sgd', loss: 'binaryCrossentropy', lr:0.1}) ...
# Let's create a Saver object # By default, the Saver handles every Variables related to the default graph all_saver = tf.train.Saver()# But you can precise which vars you want to save under which name v2_saver = tf.train.Saver({"v2": v2})# By default the Session handles ...
# Let's create a Saver object # By default, the Saver handles every Variables related to the default graph all_saver = tf.train.Saver() # But you can precise which vars you want to save under which name v2_saver = tf.train.Saver({"v2": v2}) ...