Recently, i was in an interview and i was asked to build a neural netowrk using tensorflow which meets the following requirements:

1. The input layer of the model must have an input shape of (32, 10, 1)
2. The model must have an output shape of (32, 10, 1)

and in response, i provided the following solution:

import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv1D

model = tf.keras.models.Sequential([

    Conv1D(filters=32, kernel_size=1, activation='relu', input_shape=(32, 10, 1)),

    Dense(30, activation='relu'),
    Dense(10, activation='relu'),

    tf.keras.layers.Dense(1)
])

and in order to prove that my model can pass the requeirment, i printed the input-shape and out-put shape of each model using the below code:

for layer in model.layers:
  print('input shape: ',layer.input_shape, 'output shape: ',layer.output_shape)

and here is what i got in the output:

input shape:  (None, 32, 10, 1) output shape:  (None, 32, 10, 32)
input shape:  (None, 32, 10, 32) output shape:  (None, 32, 10, 30)
input shape:  (None, 32, 10, 30) output shape:  (None, 32, 10, 10)
input shape:  (None, 32, 10, 10) output shape:  (None, 32, 10, 1)

Sadly and apparently my answer to this question was not correct and i don't know how to build such model ?

As you can see, my model has 4 dimentions and the input and output layer start by None. Is it the problem ?

CodePudding user response：

I am not 100% sure but for me really seems like you did not explicitly declared the input layer, I really think at the shape's command response we should not see a 'None' on it.

Two possible solutions I found at this source, which the best one seems to be the following (not tested):

inputs = Input(shape=(32, 10, 1)) 
x = Conv1D(filters=32, kernel_size=1)(inputs) 
x = Dense(30, "relu")(x)
outputs = Dense(10, "relu")(x) 
model = Model(inputs=inputs, outputs=outputs, name="my_model_name")

Let's see if that makes any sense.

CodePudding user response：

Thanks to @Pedro Silva and @AloneTogether i came out with a possible solution as below. So, in the Input or Conv1D layer the input_shape does not include the Batch_size of the input data. The input_shape only specifies the shape of each Data point or (entry of data) and if we need to specify the Batch_size then we cn use the batch_size parameter in the layer. So, if we develop the mode as :

import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv1D,Input
from tensorflow.keras.models import Model

model = tf.keras.models.Sequential([

    Conv1D(filters=32, kernel_size=1, activation='relu', input_shape=(10, 1),batch_size=32),

    Dense(30, activation='relu'),
    Dense(10, activation='relu'),

    tf.keras.layers.Dense(1)
])

for layer in model.layers:
  print('input shape: ',layer.input_shape, 'output shape: ',layer.output_shape)

or this:

inputs = Input(shape=(10, 1),batch_size=32) 
x = Conv1D(filters=32, kernel_size=1)(inputs) 
x = Dense(30, "relu")(x)
outputs = Dense(10, "relu")(x) 
model = Model(inputs=inputs, outputs=outputs, name="my_model_name")

for layer in model.layers:
  print('input shape: ',layer.input_shape, 'output shape: ',layer.output_shape)

Then in both cases, the model has the following shape of input and output:

input shape:  (32, 10, 1) output shape:  (32, 10, 1)
input shape:  (32, 10, 1) output shape:  (32, 10, 32)
input shape:  (32, 10, 32) output shape:  (32, 10, 30)
input shape:  (32, 10, 30) output shape:  (32, 10, 10)