# Get keras middle layer output in sequential

**feature extractor**based on your desire end product layer. Your graph gets disconnected in here

`bneck.layers[12].output`

. Let ‘s say you have `model A`

and `model B`

. And you want some layer ‘s output ( let ‘s say **2**layers ) from

`model A`

and use them in `model B`

to complete its architecture. To do that, you first gear create **2**feature extractor from

`model A`

as follows
```
extractor_one = Model(modelA.input, expected_layer_1.output)
extractor_two = Model(modelA.input, expected_layer_2.output)
```

here I will walk you through a childlike code exemplar. There can be a more **flexible and smart approach** to do this but hera is one of them. I will build a consecutive model and train it on `CIFAR10`

and next, I will try to build a functional model where I will utilize some of the consecutive exemplar layers ( just **2** of them ) and train the complete model on `CIFAR100`

.

```
import tensorflow as tf
seq_model = tf.keras.Sequential(
[
tf.keras.Input(shape=(32, 32, 3)),
tf.keras.layers.Conv2D(16, 3, activation="relu"),
tf.keras.layers.Conv2D(32, 3, activation="relu"),
tf.keras.layers.Conv2D(64, 3, activation="relu"),
tf.keras.layers.Conv2D(128, 3, activation="relu"),
tf.keras.layers.Conv2D(256, 3, activation="relu"),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(10, activation='softmax')
]
)
seq_model.summary()
```

Trian on `CIFAR10`

data jell

```
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
# train set / data
x_train = x_train.astype('float32') / 255
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
print(x_train.shape, y_train.shape)
seq_model.compile(
loss = tf.keras.losses.CategoricalCrossentropy(),
metrics = tf.keras.metrics.CategoricalAccuracy(),
optimizer = tf.keras.optimizers.Adam())
# fit
seq_model.fit(x_train, y_train, batch_size=128, epochs=5, verbose = 2)
# -------------------------------------------------------------------
(50000, 32, 32, 3) (50000, 10)
Epoch 1/5
27s 66ms/step - loss: 1.2229 - categorical_accuracy: 0.5647
Epoch 2/5
26s 67ms/step - loss: 1.1389 - categorical_accuracy: 0.5950
Epoch 3/5
26s 67ms/step - loss: 1.0890 - categorical_accuracy: 0.6127
Epoch 4/5
26s 67ms/step - loss: 1.0475 - categorical_accuracy: 0.6272
Epoch 5/5
26s 67ms/step - loss: 1.0176 - categorical_accuracy: 0.6409
```

now, let ‘ say we want some output from this consecutive model, let ‘s say of the succeed two layers.

```
tf.keras.layers.Conv2D(64, 3, activation="relu") # (None, 26, 26, 64)
tf.keras.layers.Conv2D(256, 3, activation="relu") # (None, 22, 22, 256)
```

To get them we first create **two feature extractor** from the consecutive model

```
last_layer_outputs = tf.keras.Model(seq_model.input, seq_model.layers[-3].output)
last_layer_outputs.summary() # (None, 22, 22, 256)
mid_layer_outputs = tf.keras.Model(seq_model.input, seq_model.layers[2].output)
mid_layer_outputs.summary() # (None, 26, 26, 64)
```

optionally, if we want to freeze them we can do that excessively now. Freezing because we choose the same type of data set here. ( `CIFAR 10-100`

).

```
print('last layer output')
# just freezing first 2 layer
for layer in last_layer_outputs.layers[:2]:
layer.trainable = False
# checking
for l in last_layer_outputs.layers:
print(l.name, l.trainable)
print('\nmid layer output')
# freeze all layers
mid_layer_outputs.trainable = False
# checking
for l in mid_layer_outputs.layers:
print(l.name, l.trainable)
last layer output
input_11 False
conv2d_81 False
conv2d_82 False
conv2d_83 False
conv2d_84 True
conv2d_85 True
mid layer output
input_11 False
conv2d_81 False
conv2d_82 False
conv2d_83 False
```

now, let ‘s create a new mannequin with functional API and use the above two **feature extractors** .

```
encoder_input = tf.keras.Input(shape=(32, 32, 3), name="img")
x = tf.keras.layers.Conv2D(16, 3, activation="relu")(encoder_input)
last_x = last_layer_outputs(encoder_input)
print(last_x.shape) # (None, 22, 22, 256)
mid_x = mid_layer_outputs(encoder_input)
mid_x = tf.keras.layers.Conv2D(32, kernel_size=3, strides=1)(mid_x)
print(mid_x.shape) # (None, 24, 24, 32)
last_x = tf.keras.layers.GlobalMaxPooling2D()(last_x)
mid_x = tf.keras.layers.GlobalMaxPooling2D()(mid_x)
print(last_x.shape, mid_x.shape) # (None, 256) (None, 32)
encoder_output = tf.keras.layers.Concatenate()([last_x, mid_x])
print(encoder_output.shape) # (None, 288)
encoder_output = tf.keras.layers.Dense(100, activation='softmax')(encoder_output)
print(encoder_output.shape) # (None, 100)
encoder = tf.keras.Model(encoder_input, encoder_output, name="encoder")
encoder.summary()
```

gearing on `CIFAR100`

```
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar100.load_data()
# train set / data
x_train = x_train.astype('float32') / 255
y_train = tf.keras.utils.to_categorical(y_train, num_classes=100)
print(x_train.shape, y_train.shape)
encoder.compile(
loss = tf.keras.losses.CategoricalCrossentropy(),
metrics = tf.keras.metrics.CategoricalAccuracy(),
optimizer = tf.keras.optimizers.Adam())
# fit
encoder.fit(x_train, y_train, batch_size=128, epochs=5, verbose = 1)
```

reference : sport origin with a consecutive model