65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
import os
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import keras
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import numpy as np
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import tensorflow as tf
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from stock.util.Stock2Vector import Stock2Vector
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from classification_models.keras import Classifiers
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class StockTrainer:
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RESOURCE_PATH = None
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stock2Vector = None
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def __init__(self, RESOURCE_PATH):
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self.RESOURCE_PATH = RESOURCE_PATH
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self.stock2Vector = Stock2Vector(RESOURCE_PATH)
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return
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def train(self, stock_code):
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#X, Y = self.stock2Vector.getDataset3D(stock_code)
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X, Y = self.stock2Vector.getDataset2D(stock_code)
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# build model
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n_classes = 3
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Inceptionresnetv2, preprocess_input = Classifiers.get('inceptionresnetv2')
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X = preprocess_input(X)
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# train
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checkpoint_filename = os.path.join(self.RESOURCE_PATH, "model", "stock.ckpt")
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base_model = Inceptionresnetv2(input_shape=(299, 299, 3), include_top=False)
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x = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)
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output = tf.keras.layers.Dense(n_classes, activation='softmax')(x)
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model = keras.models.Model(inputs=[base_model.input], outputs=[output])
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model.compile(optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy'])
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chekpoint = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filename, save_weights_only=True, verbose=1)
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earlystop = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5, mode="auto")
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if os.path.isfile(checkpoint_filename):
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model.load_weights(checkpoint_filename)
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model.fit(x=X,
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y=Y,
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batch_size=10000,
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epochs=10000,
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callbacks=[chekpoint, earlystop])
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return
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if __name__ == "__main__":
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PROJECT_HOME = os.path.join(os.path.dirname(__file__))
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RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
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stock_codes = {
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# 252670
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# 122630
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"252670": ['20220729'],
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}
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for stock_code in stock_codes:
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stockTrainer = StockTrainer(RESOURCE_PATH)
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stockTrainer.train(stock_code)
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print ("done...") |