42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
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import sys
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from tensorflow.keras.backend import batch_dot, mean
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import pandas as pd
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import numpy as np
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from six import int2byte
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from sklearn import preprocessing
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.layers import Input, Dense, Activation,Dropout
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from tensorflow.keras.models import Model
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from tensorflow.keras.callbacks import EarlyStopping
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from tensorflow.keras.models import Sequential
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X=pd.read_csv('10_x.csv')
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Y=pd.read_csv('10_y.csv')
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X_train, X_test, y_train, y_test = train_test_split(X,Y , test_size=0.2,train_size=0.8, random_state=21)
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model = Sequential()
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model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
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model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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model.fit(X_train, y_train, epochs=15, batch_size=16, validation_data=(X_test, y_test))
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prediction = model.predict(X_test)
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rmse = mean_squared_error(y_test, prediction)
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model.save('vgsales_model_dvc.h5')
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