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统一 frontend、backend、test、test-log 和 out 目录，更新构建、部署及测试入口；纳入配置管理兼容与首次安全引导修复；排除本机配置、密钥和测试产物。
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
import retvec
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import datasets
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # silence TF INFO messages
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import tensorflow as tf
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import numpy as np
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from tensorflow.keras import layers
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from retvec.tf import RETVecTokenizer
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NUM_CLASSES = 3
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def getData(folder_path):
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labels = []
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msgs = []
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# 遍历文件夹
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for root, dirs, files in os.walk(folder_path):
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# 遍历当前文件夹下的所有文件
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for filename in files:
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# 判断是否为csv文件
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if filename.endswith(".csv"):
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file_path = os.path.join(root, filename)
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# 读取csv文件内容
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with open(file_path, 'r', errors='ignore') as csv_file:
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for line in csv_file:
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if line[0] == '' or line[0]==' ':
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continue
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labels.append([int(str.strip(line[0]))])
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msgs.append(line[3:])
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return np.array(msgs), np.array(labels)
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trainDataMsgs, trainDataLabels = getData("./trainData")
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testDataMsgs, testDataLabels = getData("./testData")
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# preparing data
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x_train = tf.constant(trainDataMsgs, dtype=tf.string)
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print(x_train.shape)
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y_train = np.zeros((len(x_train),NUM_CLASSES))
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for idx, ex in enumerate(trainDataLabels):
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for val in ex:
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y_train[idx][val] = 1
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# test data
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x_test = tf.constant(testDataMsgs, dtype=tf.string)
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y_test = np.zeros((len(x_test),NUM_CLASSES))
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for idx, ex in enumerate(testDataLabels):
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for val in ex:
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y_test[idx][val] = 1
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# using strings directly requires to put a shape of (1,) and dtype tf.string
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inputs = layers.Input(shape=(1,), name="token", dtype=tf.string)
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# add RETVec tokenizer layer with default settings -- this is all you have to do to build a model with RETVec!
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x = RETVecTokenizer(model='retvec-v1')(inputs)
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# standard two layer LSTM
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x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x)
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x = layers.Bidirectional(layers.LSTM(64))(x)
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outputs = layers.Dense(NUM_CLASSES, activation='sigmoid')(x)
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model = tf.keras.Model(inputs, outputs)
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model.summary()
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# compile and train the model
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batch_size = 256
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epochs = 2
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model.compile('adam', 'binary_crossentropy', ['acc'])
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history = model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,
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validation_data=(x_test, y_test))
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# saving the model
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save_path = './emotion_model/1'
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model.save(save_path)
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