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import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import make_column_transformer, make_column_selector
from sklearn.model_selection import train_test_split
fuel = pd.read_csv('../input/dl-course-data/fuel.csv')
X = fuel.copy()
# Remove target
y = X.pop('FE')
preprocessor = make_column_transformer(
(StandardScaler(),
make_column_selector(dtype_include=np.number)),
(OneHotEncoder(sparse=False),
make_column_selector(dtype_include=object)),
)
X = preprocessor.fit_transform(X)
y = np.log(y) # log transform target instead of standardizing
input_shape = [X.shape[1]]
print("Input shape: {}".format(input_shape))
from tensorflow import keras
from tensorflow.keras import layers
model = keras.Sequential([
layers.Dense(128, activation='relu', input_shape=input_shape),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(1),
])
model.compile(
optimizer='adam',
loss='mae',
)
history = model.fit(
X, y,
batch_size=128,
epochs=200,
)
import pandas as pd
history_df = pd.DataFrame(history.history)
# Start the plot at epoch 5. You can change this to get a different view.
history_df.loc[5:, ['loss']].plot();
# YOUR CODE HERE: Experiment with different values for the learning rate, batch size, and number of examples
learning_rate = 0.05
batch_size = 32
num_examples = 256
animate_sgd(
learning_rate=learning_rate,
batch_size=batch_size,
num_examples=num_examples,
# You can also change these, if you like
steps=50, # total training steps (batches seen)
true_w=3.0, # the slope of the data
true_b=2.0, # the bias of the data
)
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