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revert to what works locally

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jakubknczny 2021-05-16 13:01:57 +02:00
parent ff15eb3bca
commit 7ef330e804
2 changed files with 3 additions and 31 deletions
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30
lab5/eval/eval.py
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@ -4,43 +4,19 @@ import seaborn as sns
import sys import sys
import tensorflow import tensorflow
from tensorflow.keras import layers from tensorflow.keras import layers
# from tensorflow.keras.models import load_model from tensorflow.keras.models import load_model
# X_test = pd.read_csv('test.csv')
#
# Y_test = X_test.pop('stabf')
# Y_test = pd.get_dummies(Y_test)
#
# model = load_model('grid-stability-dense.h5')
X_train = pd.read_csv('train.csv')
X_test = pd.read_csv('test.csv') X_test = pd.read_csv('test.csv')
X_valid = pd.read_csv('valid.csv')
Y_train = X_train.pop('stabf')
Y_train = pd.get_dummies(Y_train)
Y_test = X_test.pop('stabf') Y_test = X_test.pop('stabf')
Y_test = pd.get_dummies(Y_test) Y_test = pd.get_dummies(Y_test)
Y_valid = X_valid.pop('stabf') model = load_model('grid-stability-dense.h5')
Y_valid = pd.get_dummies(Y_valid)
model = tensorflow.keras.Sequential([
layers.Input(shape=(12,)),
layers.Dense(32),
layers.Dense(16),
layers.Dense(2, activation='softmax')
])
model.compile(
loss=tensorflow.keras.losses.BinaryCrossentropy(),
optimizer=tensorflow.keras.optimizers.Adam(),
metrics=[tensorflow.keras.metrics.BinaryAccuracy()])
history = model.fit(X_train, Y_train, epochs=2, validation_data=(X_valid, Y_valid))
results = model.evaluate(X_test, Y_test, batch_size=64) results = model.evaluate(X_test, Y_test, batch_size=64)
with open('eval.csv', 'a', newline='') as fp: with open('eval.csv', 'a', newline='') as fp:
wr = csv.writer(fp, dialect='excel') wr = csv.writer(fp, dialect='excel')
wr.writerow(results) wr.writerow(results)

4
lab5/train/train.py
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@ -4,15 +4,11 @@ import tensorflow
from tensorflow.keras import layers from tensorflow.keras import layers
X_train = pd.read_csv('train.csv') X_train = pd.read_csv('train.csv')
X_test = pd.read_csv('test.csv')
X_valid = pd.read_csv('valid.csv') X_valid = pd.read_csv('valid.csv')
Y_train = X_train.pop('stabf') Y_train = X_train.pop('stabf')
Y_train = pd.get_dummies(Y_train) Y_train = pd.get_dummies(Y_train)
Y_test = X_test.pop('stabf')
Y_test = pd.get_dummies(Y_test)
Y_valid = X_valid.pop('stabf') Y_valid = X_valid.pop('stabf')
Y_valid = pd.get_dummies(Y_valid) Y_valid = pd.get_dummies(Y_valid)