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[Keras Applications] Change to tf.keras import #4

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May 8, 2020
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[Keras Applications] Change to tf.keras import #4

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34 changes: 17 additions & 17 deletions templates/api/applications/index.md
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Original file line number Diff line number Diff line change
Expand Up @@ -54,9 +54,9 @@ Depth refers to the topological depth of the network. This includes activation l
### Classify ImageNet classes with ResNet50

```python
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

model = ResNet50(weights='imagenet')
Expand All @@ -77,9 +77,9 @@ print('Predicted:', decode_predictions(preds, top=3)[0])
### Extract features with VGG16

```python
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
import numpy as np

model = VGG16(weights='imagenet', include_top=False)
Expand All @@ -96,10 +96,10 @@ features = model.predict(x)
### Extract features from an arbitrary intermediate layer with VGG19

```python
from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.models import Model
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg19 import preprocess_input
from tensorflow.keras.models import Model
import numpy as np

base_model = VGG19(weights='imagenet')
Expand All @@ -117,10 +117,10 @@ block4_pool_features = model.predict(x)
### Fine-tune InceptionV3 on a new set of classes

```python
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D

# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
Expand Down Expand Up @@ -165,7 +165,7 @@ for layer in model.layers[249:]:

# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
from tensorflow.keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')

# we train our model again (this time fine-tuning the top 2 inception blocks
Expand All @@ -177,8 +177,8 @@ model.fit(...)
### Build InceptionV3 over a custom input tensor

```python
from keras.applications.inception_v3 import InceptionV3
from keras.layers import Input
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.layers import Input

# this could also be the output a different Keras model or layer
input_tensor = Input(shape=(224, 224, 3))
Expand Down