Introduction to Deep Learning with Keras
Miguel Esteban
Data Scientist & Founder
import seaborn as sns
# Plot a pairplot
sns.pairplot(circles, hue="target")
from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense
# Instantiate a sequential model model = Sequential()
from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense
# Instantiate a sequential model model = Sequential()
# Add input and hidden layer model.add(Dense(4, input_shape=(2,), activation='tanh'))
from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense
# Instantiate a sequential model model = Sequential()
# Add input and hidden layer model.add(Dense(4, input_shape=(2,), activation='tanh'))
# Add output layer, use sigmoid model.add(Dense(1, activation='sigmoid'))
# Compile model model.compile(optimizer='sgd', loss='binary_crossentropy')
# Train model model.train(coordinates, labels, epochs=20)
# Predict with trained model preds = model.predict(coordinates)
Introduction to Deep Learning with Keras