In this post, we will learn how to determine input shape in Keras TensorFlow. Understanding the input shape is crucial when building neural networks in Keras. It helps to define the dimensions of the data that your model will process.
Step 1 : Inspecting Your Data
For Numerical Data:
- We are using Python’s shape attribute to check the dimensions of your NumPy array or Pandas
Data Frame:
import numpy as np data = np.array([[1, 2, 3], [4, 5, 6]]) print(data.shape) # Output: (2, 3)
- This indicates a 2D array with 2 samples and 3 features.
For Image Data:
- Let’s consider the image dimensions (height, width, and channels):
-For grayscale images: (height, width)
-For RGB images: (height, width, 3) - or , Use libraries like OpenCV or Pillow to load and inspect Images:
from PIL import Image
img = Image.open('image.jpg')
print(img.size) # Output: (256, 256)
Step 2: Analyzing the First Layer of Your Model
For Sequential Models:
- Here , specify the input shape directly in the first layer:
from keras.models import Sequential from keras.layers import Dense model = Sequential() model.add(Dense(64, input_shape=(784,)))
- Here, the input shape is (784,), meaning 784 features.
For Functional API Models:
- We have to define an input layer with the desired shape:
from keras.layers import Input from keras.models import Model inputs = Input(shape=(784,)) # ... rest of the model
Step 3 : Pre-trained Models:
- Pre-trained models often have specific input shape requirements.
- Consult the model’s documentation or use the model.summary() method to check the expected input shape.
Conclusion:
By concluding we can say that , the input shape in Keras refers to the dimensions of the data fed into your model. It’s a key parameter that determines how the model processes the data. We should keep in mind that input shape does not include the batch size, which is handled separately. Testing various input shapes can help you achieve better model performance.