56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
import pandas as pd
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import tensorflow as tf
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from keras.models import Sequential
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from keras.layers import Dense
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from keras import utils
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import os
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EPOCHS = int(os.environ['EPOCHS'])
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if EPOCHS <= 0:
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EPOCHS = 1000
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X_train = pd.read_csv('./X_train.csv',
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engine = 'python',
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encoding = 'ISO-8859-1',
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sep=',')
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X_val = pd.read_csv('./X_val.csv',
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engine = 'python',
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encoding = 'ISO-8859-1',
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sep=',')
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Y_train = pd.read_csv('./Y_train.csv',
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engine = 'python',
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encoding = 'ISO-8859-1',
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sep=',')
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Y_val = pd.read_csv('./Y_val.csv',
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engine = 'python',
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encoding = 'ISO-8859-1',
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sep=',')
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Y_train = utils.to_categorical(Y_train)
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Y_val = utils.to_categorical(Y_val)
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model = Sequential(
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[
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Dense(100, input_dim=X_train.shape[1], activation='relu'),
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Dense(70, activation='relu'),
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Dense(50, activation='relu'),
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Dense(4, activation='softmax')
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], name = "Powerlifters_model"
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)
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model.compile(
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loss=tf.keras.losses.CategoricalCrossentropy(),
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optimizer=tf.keras.optimizers.Adam(),
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metrics=['accuracy'])
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model.fit(
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X_train,Y_train,
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epochs = EPOCHS,
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validation_data=(X_val, Y_val)
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)
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model.save('model') |