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Introducción al Deep Learning con Keras

Miguel Esteban

Data Scientist & Founder

Resumen

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))
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Introducción al Deep Learning con Keras

Compilar

# Compiling your previously built model
model.compile(optimizer="adam", loss="mse")

Introducción al Deep Learning con Keras

Entrenar

# 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
Introducción al Deep Learning con Keras

Predecir

# 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]])
Introducción al Deep Learning con Keras

Evaluar

# Evaluate your results
model.evaluate(X_test, y_test)

1000/1000 [==============================] - 0s 53us/step
0.25
Introducción al Deep Learning con Keras

El problema actual

Introducción al Deep Learning con Keras

Predicción científica

Introducción al Deep Learning con Keras

Tu tarea

Introducción al Deep Learning con Keras

¡Vamos a salvar la Tierra!

Introducción al Deep Learning con Keras

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