How to get the input shape of TensorFlow layers?

TensorFlow
1

Having a model instance as input, I would like to create a function that prints the input shape of the model layers. I am aware of model.layers() but this is not general enough for what I want. Layers in my models might not have a sequential order, and a layer might receive its input from a layer different than the one preceding it. I would also to have tensorops included in case they are present in the model.

1 Like
3

Hi @Nada-Nada, You can iterate through the layers in the model and can get the input shapes of the layers. For example,

input1 = keras.Input(shape=(128,))
input2 = keras.Input(shape=(64,))
dense1 = keras.layers.Dense(64)(input1)
dense2 = keras.layers.Dense(32)(input2)
concat = keras.layers.Concatenate()([dense1, dense2])
output = keras.layers.Dense(10)(concat)

model = keras.Model(inputs=[input1, input2], outputs=output)

for layer in model.layers:
    print(layer.input)

Thank You.

4

Hi @Kiran_Sai_Ramineni , Thank you for your answer. My model has this architecture:

class NeuralNetwork(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.l1 = layers.Embedding(input_dim=5000, output_dim=50)
        self.l2 = layers.Dropout(rate=0.5)
        self.l3 = layers.Conv1D(filters=200, kernel_size=4, strides=1, padding='valid', activation='relu')
        self.l4 = layers.MaxPool1D(pool_size=2, strides=2, padding='valid')
        self.l5 = layers.Conv1D(filters=200, kernel_size=5, strides=1, padding='valid', activation='relu')
        self.l6 = layers.MaxPool1D(pool_size=2, strides=2, padding='valid')
        self.l7 = layers.Dropout(rate=0.15)
        self.l8 = layers.GRU(units=100, activation=None, dropout=0.0)
        self.l9 = layers.Dense(units=400, activation='relu')
        self.l10 = layers.Dropout(rate=0.1)
        self.l11 = layers.Dense(units=1, activation='sigmoid')

    def call(self, x):
        x = self.l1(x)
        x = self.l2(x)
        x_1 = self.l3(x)
        x_1 = self.l4(x_1)
        x_2 = self.l5(x)
        x_2 = self.l6(x_2)
        x_2 = tf.concat([x_1, x_2], axis=1)
        x_2 = self.l7(x_2)
        x_2 = self.l8(x_2)
        x_2 = self.l9(x_2)
        x_2 = self.l10(x_2)
        x_2 = self.l11(x_2)
        return x_2

x = tf.random.uniform((1, 299))
model = NeuralNetwork()
out = model(x)

Unfortunately, model.layers is not giving me the correct input shapes.

I also cannot change how the model is defined.

Thank you

6

Hi @Nada-Nada, I have tried in an alternative way by creating a dictionary in the class by adding the layer name and input and able to get those. please refer to this gist for working code example. Thank You.

7

Thank you @Kiran_Sai_Ramineni, much appreciated.