QAQwhq的博客

Linux下绘制模型结构图出现报错

除了用tensorboard，也可如此如此history = model.fit(generate_data(npy_path + '/train'), steps_per_epoch=len(os.listdir(npy_path + '/train')) // 3, epochs=epochs, validation_data=generate_valid_data(npy_pa

推理import osos.environ['CUDA_VISIBLE_DEVICES'] = '0, 1' # 选择GPU设备predict = model.predict(SignalSeq_16[:10])训练os.environ['CUDA_VISIBLE_DEVICES'] = '0'gpus = tf.config.experimental.list_physical_devices(device_type='GPU')for gpu in gpus: tf.config

TensorFlow的画图功能非常强大，可以在模型设计完后进行检验、总结与展示。tf调用该函数如下所示：tf.keras.utils.plot_model(model, to_file='model.png')除此调用可能会缺少包：ImportError: Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.在Windows环境下，用conda命令安装即可。conda inst

preprocessing.LabelEncoder()使用e.g. 1：from sklearn import preprocessingle = preprocessing.LabelEncoder()arr_gf = [1,2,3,'wom','wom','中文','中文']le.fit(arr_gf)one_hot_gf = le.transform(arr_gf)print(one_hot_gf)输出：[0 1 2 3 3 4 4]e.g. 2:csv_path = './

def load_pretrain_embedding(goods_path, seller_path): model = build_model() goods_model = tf.keras.models.load_model(goods_path) # 预训练模型 for i, pretrain_layer in enumerate(goods_model.layers): if isinstance(pretrain_layer, tf.keras

载入数据input1 = np.load('/root/whq/input/one_hot_1400/xf_p_n.npy')input2 = np.load('/root/whq/input/one_hot_1400/gf_p_n.npy')output_ = np.load('/root/whq/input/one_hot_1400/label_p_n_-1.npy')output_0 = np.load('/root/whq/input/one_hot_1400/label_p_n_0.npy