Understanding and Fixing the "Input Incompatible with Layer" Error in TensorFlow Keras
This article will delve into the common "Input Incompatible with Layer" error in TensorFlow Keras, focusing on an example involving image feature fusion using pre-trained InceptionV3 and VGG16 models. We will dissect the error message, explore its root cause, and provide a solution to ensure smooth model training.
The Error Message
The error "Input 1 of layer "model_5" is incompatible with the layer: expected shape=(None, 224, 224, 3), found shape=(None, 5)" indicates a mismatch between the input shape expected by a layer and the actual input shape provided during model training. In this specific case, the error points to the model_5 layer, which expects an input of shape (None, 224, 224, 3) – a batch of images with dimensions 224x224 pixels and three color channels – but instead receives a shape of (None, 5).
Decoding the Problem
The issue stems from the code provided in the question. Let's analyze it step-by-step:
- Pre-trained Model Loading: The code loads the InceptionV3 and VGG16 models without the top classification layers (include_top=False), which are commonly used for feature extraction.
- Input Layers: Two input layers are defined:
inp_inputfor InceptionV3 andvgg_inputfor VGG16. Both have different input shapes reflecting the pre-trained model's requirements. - Feature Extraction: The pre-trained models are applied to their respective inputs (
inp_inputandvgg_input), extracting features. - Global Average Pooling: The extracted features are then processed using Global Average Pooling, resulting in flattened feature vectors.
- Concatenation: The flattened feature vectors from both models are concatenated along the last axis using
tf.keras.layers.concatenate. This combines the features from both models. - Final Dense Layer: Finally, the concatenated features are passed through a dense layer with a softmax activation to generate the final predictions.
The Source of the Error
The error arises because the concatenated features (shape (None, 2560)) are fed into the dense layer. The dense layer expects an image input of shape (None, 224, 224, 3). This mismatch occurs because the pre-trained models have been trained on images, and the final dense layer expects to receive image data. This is where the feature fusion process diverges from the original intended use of the pre-trained models, leading to the incompatibility.
Solution
The solution involves modifying the code to provide the final dense layer with an appropriate input shape. The following approach can be employed:
-
Reshaping: Reshape the concatenated features to a shape expected by the dense layer before applying it. For example, reshape it to
(None, 224, 224, 3), assuming you want the final dense layer to process image data. -
Removing the dense layer: If the final dense layer is not meant to process images, remove it. If it's meant to classify your fused features, use an appropriate number of units in the dense layer and adjust the input shape accordingly.
Here's the modified code snippet:
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.vgg16 import VGG16
import tensorflow as tf
from tensorflow.keras import layers, Input
inp_pre_trained_model = InceptionV3(include_top=False)
inp_pre_trained_model.trainable = False
inp_input = tf.keras.Input(shape=(299, 299, 3), name="input_layer_inception_V3")
inp_x = inp_pre_trained_model(inp_input)
inp_x = layers.GlobalAveragePooling2D(name="global_average_pooling_layer_inception_v3")(inp_x)
vgg_pre_trained_model = VGG16(include_top=False)
vgg_pre_trained_model.trainable = False
vgg_input = tf.keras.Input(shape=(224, 224, 3), name="input_layer_VGG_16")
vgg_x = vgg_pre_trained_model(vgg_input)
vgg_x = layers.GlobalAveragePooling2D(name="global_average_pooling_layer_vgg_16")(vgg_x)
x = tf.keras.layers.concatenate([inp_x, vgg_x], axis=-1)
# Reshape the concatenated features
x = tf.keras.layers.Reshape((224, 224, 3))(x) # Adjust this shape as needed
x = tf.keras.layers.Flatten()(x)
# Adjust the dense layer units and activation as per your task
outputs = tf.keras.layers.Dense(10, activation="softmax", name="output_layer")(x)
model = tf.keras.Model(inputs=[inp_input, vgg_input], outputs=outputs)
model.summary()
Key Takeaway
The "Input Incompatible with Layer" error highlights the critical importance of aligning the shapes of inputs and layers in TensorFlow Keras. Carefully reviewing the expected input shapes and the actual data provided during model training will help you identify and resolve such errors. Remember to understand the inherent characteristics of each layer and how they expect data to be fed into them for smooth model execution.
