Sequential Model, Dense Layer, and Model Compile in Keras Deep Learning
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9 ماه پیش
-
- The Sequential API in
- The Sequential API in Keras is a stack of layers, where you can simply add one layer at a time.
- Each layer has weights that correspond to the layer that follows it.
- It's a straightforward way to build and train models.
- For more complex architectures, you might want to explore the Functional API in Keras.
- Here's a concise breakdown of how it works:
1. Initialize model
```
from keras.models import Sequential
model = Sequential()
```
2. Add layers
```
from keras.layers import Dense
Add input layer
model.add(Dense(units=... , input_dim=... , activation=...))
Add hidden layers
model.add(Dense(units=... , activation=...))
Add output layer
model.add(Dense(units=... , activation=...))
```
3. Compile model
```
model.compile(optimizer=..., loss=..., metrics=[...])
```
4. Train model
```
model.fit(X_train, y_train, epochs=..., batch_size=...)
```
5. Make predictions
```
predictions = model.predict(new_data)
#tensorflow #neuralnetworks #deeplearning
```
- Each layer has weights that correspond to the layer that follows it.
- It's a straightforward way to build and train models.
- For more complex architectures, you might want to explore the Functional API in Keras.
- Here's a concise breakdown of how it works:
1. Initialize model
```
from keras.models import Sequential
model = Sequential()
```
2. Add layers
```
from keras.layers import Dense
Add input layer
model.add(Dense(units=... , input_dim=... , activation=...))
Add hidden layers
model.add(Dense(units=... , activation=...))
Add output layer
model.add(Dense(units=... , activation=...))
```
3. Compile model
```
model.compile(optimizer=..., loss=..., metrics=[...])
```
4. Train model
```
model.fit(X_train, y_train, epochs=..., batch_size=...)
```
5. Make predictions
```
predictions = model.predict(new_data)
#tensorflow #neuralnetworks #deeplearning
```
9 ماه پیش
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