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keras_regression.py
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keras_regression.py
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# coding: utf-8
# Keras Regression example using
# - Sequential model
# - Functional API
# In[11]:
from ds_utils.imports import *
# In[12]:
boston = sklearn.datasets.load_boston()
X = boston.data
y = boston.target
# ## Benchmark
# In[13]:
sklearn.model_selection.cross_val_score(
sklearn.dummy.DummyRegressor(), X, y, scoring='neg_mean_squared_error')
# In[14]:
sklearn.model_selection.cross_val_score(
xgb.XGBRegressor(), X, y, scoring='neg_mean_squared_error')
# ## Sequential model
# In[15]:
model = keras.models.Sequential()
model.add(keras.layers.Dense(32, activation='relu', input_dim=X.shape[1]))
model.add(keras.layers.Dense(16, activation='relu'))
model.add(keras.layers.Dense(1))
# In[7]:
model.compile(optimizer='adam', loss = 'mean_squared_error')
model.fit(X, y, epochs=100, batch_size=32)
# In[18]:
y_pred = model.predict(X)
# In[17]:
sklearn.metrics.mean_squared_error(y, y_pred)
# ## Functional KPI
# In[23]:
inputs = keras.layers.Input(shape=(X.shape[1],))
x = keras.layers.Dense(32, activation='relu')(inputs)
x = keras.layers.Dense(16, activation='relu')(x)
predictions = keras.layers.Dense(1)(x)
model = keras.models.Model(inputs=inputs, outputs=predictions)
model.compile(optimizer='adam',
loss='mean_squared_error')
model.fit(X, y, epochs=100, batch_size=1)
# Reference: http://machinelearningmastery.com/regression-tutorial-keras-deep-learning-library-python/