Simplify Keras model definition
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 23 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 23 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 23 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 23 mins ago
spicyramen
265311
asked 23 mins ago
spicyramen
265311
asked 23 mins ago
spicyramen
265311
asked 23 mins ago
spicyramen
265311
asked 23 mins ago
spicyramen
265311
265311
add a comment |
add a comment |
active
oldest
votes
active
oldest
votes
active
oldest
votes
active
oldest
votes
active
oldest
votes
draft saved
draft discarded
Thanks for contributing an answer to Code Review Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Some of your past answers have not been well-received, and you're in danger of being blocked from answering.
Please pay close attention to the following guidance:
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fcodereview.stackexchange.com%2fquestions%2f209196%2fsimplify-keras-model-definition%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
