1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
## Model visualization
Keras provides utility functions to plot a Keras model (using `graphviz`).
This will plot a graph of the model and save it to a file:
```python
from keras.utils import plot_model
plot_model(model, to_file='model.png')
```
`plot_model` takes four optional arguments:
- `show_shapes` (defaults to False) controls whether output shapes are shown in the graph.
- `show_layer_names` (defaults to True) controls whether layer names are shown in the graph.
- `expand_nested` (defaults to False) controls whether to expand nested models into clusters in the graph.
- `dpi` (defaults to 96) controls image dpi.
You can also directly obtain the `pydot.Graph` object and render it yourself,
for example to show it in an ipython notebook :
```python
from IPython.display import SVG
from keras.utils import model_to_dot
SVG(model_to_dot(model).create(prog='dot', format='svg'))
```
## Training history visualization
The `fit()` method on a Keras `Model` returns a `History` object. The `History.history` attribute is a dictionary recording training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable). Here is a simple example using `matplotlib` to generate loss & accuracy plots for training & validation:
```python
import matplotlib.pyplot as plt
history = model.fit(x, y, validation_split=0.25, epochs=50, batch_size=16, verbose=1)
# Plot training & validation accuracy values
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
```
|
