Introduction to Deep Learning with Keras
Miguel Esteban
Data Scientist & Founder
from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # Create a new sequential model model = Sequential()
# Add and input and dense layer model.add(Dense(2, input_shape=(3,), activation="relu")) # Add a final 1 neuron layer model.add(Dense(1)) <
# Compiling your previously built model
model.compile(optimizer="adam", loss="mse")
# Train your model
model.fit(X_train, y_train, epochs=5)
Epoch 1/5
1000/1000 [==============================] - 0s 242us/step - loss: 0.4090
Epoch 2/5
1000/1000 [==============================] - 0s 34us/step - loss: 0.3602
Epoch 3/5
1000/1000 [==============================] - 0s 37us/step - loss: 0.3223
Epoch 4/5
1000/1000 [==============================] - 0s 34us/step - loss: 0.2958
Epoch 5/5
1000/1000 [==============================] - 0s 33us/step - loss: 0.2795
# Predict on new data
preds = model.predict(X_test)
# Look at the predictions
print(preds)
array([[0.6131608 ],
[0.5175948 ],
[0.60209155],
...,
[0.55633 ],
[0.5305591 ],
[0.50682044]])
# Evaluate your results
model.evaluate(X_test, y_test)
1000/1000 [==============================] - 0s 53us/step
0.25
Introduction to Deep Learning with Keras