Keras is present within the Tensorflow package. It can be accessed using the below line of code.
import tensorflow from tensorflow import keras
The Keras functional API helps create models that are more flexible in comparison to models created using sequential API. The functional API can work with models that have non-linear topology, can share layers and work with multiple inputs and outputs. A deep learning model is usually a directed acyclic graph (DAG) that contains multiple layers. The functional API helps build the graph of layers.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook. Following is the code snippet;
Example
print("Toy ResNet model for CIFAR10")
print("Layers generated for model")
inputs = keras.Input(shape=(32, 32, 3), name="img")
x = layers.Conv2D(32, 3, activation="relu")(inputs)
x = layers.Conv2D(64, 3, activation="relu")(x)
block_1_output = layers.MaxPooling2D(3)(x)
x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_1_output)
x = layers.Conv2D(64, 3, activation="relu", padding="same")(x)
block_2_output = layers.add([x, block_1_output])
x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_2_output)
x = layers.Conv2D(64, 3, activation="relu", padding="same")(x)
block_3_output = layers.add([x, block_2_output])
x = layers.Conv2D(64, 3, activation="relu")(block_3_output)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(256, activation="relu")(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(10)(x)
model = keras.Model(inputs, outputs, name="toy_resnet")
print("More information about the model")
model.summary()Code credit − https://www.tensorflow.org/guide/keras/functional
Output
Toy ResNet model for CIFAR10 Layers generated for model More information about the model Model: "toy_resnet" ________________________________________________________________________________ __________________ Layer (type) Output Shape Param # Connected to ================================================================================ ================== img (InputLayer) [(None, 32, 32, 3)] 0 ________________________________________________________________________________ __________________ conv2d_32 (Conv2D) (None, 30, 30, 32) 896 img[0][0] ________________________________________________________________________________ __________________ conv2d_33 (Conv2D) (None, 28, 28, 64) 18496 conv2d_32[0][0] ________________________________________________________________________________ __________________ max_pooling2d_8 (MaxPooling2D) (None, 9, 9, 64) 0 conv2d_33[0][0] ________________________________________________________________________________ __________________ conv2d_34 (Conv2D) (None, 9, 9, 64) 36928 max_pooling2d_8[0][0] ________________________________________________________________________________ __________________ conv2d_35 (Conv2D) (None, 9, 9, 64) 36928 conv2d_34[0][0] ________________________________________________________________________________ __________________ add_12 (Add) (None, 9, 9, 64) 0 conv2d_35[0][0] max_pooling2d_8[0][0] ________________________________________________________________________________ __________________ conv2d_36 (Conv2D) (None, 9, 9, 64) 36928 add_12[0][0] ________________________________________________________________________________ __________________ conv2d_37 (Conv2D) (None, 9, 9, 64) 36928 conv2d_36[0][0] ________________________________________________________________________________ __________________ add_13 (Add) (None, 9, 9, 64) 0 conv2d_37[0][0] add_12[0][0] ________________________________________________________________________________ __________________ conv2d_38 (Conv2D) (None, 7, 7, 64) 36928 add_13[0][0] ________________________________________________________________________________ __________________ global_average_pooling2d_1 (Glo (None, 64) 0 conv2d_38[0][0] ________________________________________________________________________________ __________________ dense_40 (Dense) (None, 256) 16640 global_average_pooling2d_1[0][0] ________________________________________________________________________________ __________________ dropout_2 (Dropout) (None, 256) 0 dense_40[0][0] ________________________________________________________________________________ __________________ dense_41 (Dense) (None, 10) 2570 dropout_2[0][0] ================================================================================ ================== Total params: 223,242 Trainable params: 223,242 Non-trainable params: 0 ________________________________________________________________________________ __________________
Explanation
The model has multiple inputs and outputs.
The functional API makes it easy to work with non-linear connectivity topologies.
This model with layers is not connected sequentially, hence the ‘Sequential’ API can’t work with it.
This is where residual connections come into the picture.
A sample ResNet model using CIFAR10 is built to demonstrate the same.