Talha Javed Mukhtar
commited on
Commit
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a32fc23
1
Parent(s):
36beb2f
First push
Browse files- AVAXUSDT_x22.xlsx_binary_classification_model.h5 +0 -0
- README.md +1 -0
- model.py +24 -0
AVAXUSDT_x22.xlsx_binary_classification_model.h5
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Binary file (77.6 kB). View file
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README.md
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Simple NN that takes in 27 parameters related to a cryptocurrency and classifies the datapoint as 0 OR 1.
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model.py
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import tensorflow as tf
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class CryptoBinaryClassifier(tf.keras.Model):
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def __init__(self, *args, **kwargs):
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super(CryptoBinaryClassifier, self).__init__()
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# Define your model architecture here
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self.model = tf.keras.Sequential([
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tf.keras.layers.Input(shape=(27,)),
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tf.keras.layers.Dense(64, activation='relu'),
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tf.keras.layers.Dense(32, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid')
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])
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def call(self, inputs, training=False):
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return self.model(inputs)
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def __init__(self, *args, **kwargs):
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super(CryptoBinaryClassifier, self).__init__()
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# Load your pre-trained weights
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self.load_weights('AVAXUSDT_x22.xlsx_binary_classification_model.h5')
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def predict(self, input_data):
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# Preprocess input_data if necessary
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return self(input_data)
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