Simplify Keras model definition
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Create Keras model, is there a cleaner way to simplify creating a Keras model? Similar to PyTorch? Class based.
def create_model(input_dim, learning_rate):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(100, activation=tf.nn.relu, kernel_initializer='uniform', input_shape=(input_dim,)))
model.add(tf.keras.layers.Dense(75, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation=tf.nn.sigmoid))
optimizer = tf.keras.optimizers.RMSprop(lr=learning_rate, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
return model
python tensorflow
asked 11 mins ago
spicyramen
265311
add a comment |
up vote
0
down vote
favorite
Create Keras model, is there a cleaner way to simplify creating a Keras model? Similar to PyTorch? Class based.
def create_model(input_dim, learning_rate):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(100, activation=tf.nn.relu, kernel_initializer='uniform', input_shape=(input_dim,)))
model.add(tf.keras.layers.Dense(75, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation=tf.nn.sigmoid))
optimizer = tf.keras.optimizers.RMSprop(lr=learning_rate, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
return model
python tensorflow
asked 11 mins ago
spicyramen
265311
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
Create Keras model, is there a cleaner way to simplify creating a Keras model? Similar to PyTorch? Class based.
def create_model(input_dim, learning_rate):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(100, activation=tf.nn.relu, kernel_initializer='uniform', input_shape=(input_dim,)))
model.add(tf.keras.layers.Dense(75, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation=tf.nn.sigmoid))
optimizer = tf.keras.optimizers.RMSprop(lr=learning_rate, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
return model
python tensorflow
asked 11 mins ago
spicyramen
265311
Create Keras model, is there a cleaner way to simplify creating a Keras model? Similar to PyTorch? Class based.
def create_model(input_dim, learning_rate):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(100, activation=tf.nn.relu, kernel_initializer='uniform', input_shape=(input_dim,)))
model.add(tf.keras.layers.Dense(75, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(50, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(25, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation=tf.nn.sigmoid))
optimizer = tf.keras.optimizers.RMSprop(lr=learning_rate, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
return model
python tensorflow
python tensorflow
asked 11 mins ago
spicyramen
265311
asked 11 mins ago
spicyramen
265311
asked 11 mins ago
spicyramen
265311
asked 11 mins ago
spicyramen
265311
asked 11 mins ago
spicyramen
265311
265311
add a comment |
add a comment |
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