import tensorflow as tf from tensorflow.python.keras.engine.input_spec import InputSpec # Define an InputSpec object with shape and dtype of input input_spec = InputSpec(shape=(None, 28, 28, 1), dtype=tf.float32) # Define a model using the InputSpec object inputs = tf.keras.layers.Input(shape=input_spec.shape[1:], dtype=input_spec.dtype) x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu')(inputs) x = tf.keras.layers.MaxPooling2D((2, 2))(x) x = tf.keras.layers.Flatten()(x) outputs = tf.keras.layers.Dense(10, activation='softmax')(x) model = tf.keras.models.Model(inputs=inputs, outputs=outputs) # Validate inputs using the InputSpec object model.build(input_shape=(None, 28, 28, 1)) model.summary()
import tensorflow as tf from tensorflow.python.keras.engine.input_spec import InputSpec # Define an InputSpec object with shape and dtype of input input_spec = InputSpec(shape=(None, None), dtype=tf.int32) # Define a model using the InputSpec object inputs = tf.keras.layers.Input(shape=input_spec.shape[1:], dtype=input_spec.dtype) x = tf.keras.layers.Embedding(input_dim=1000, output_dim=64)(inputs) x = tf.keras.layers.LSTM(128)(x) outputs = tf.keras.layers.Dense(10, activation='softmax')(x) model = tf.keras.models.Model(inputs=inputs, outputs=outputs) # Validate inputs using the InputSpec object model.build(input_shape=(None, None)) model.summary()In this example, we define an InputSpec object with shape `(None, None)` and dtype `tf.int32`. We use this object to define a model for sequence classification using an embedding layer and LSTM layer. When we build the model and call `model.summary()`, the input shape and dtype are validated against the InputSpec object. The package library used in these examples is TensorFlow.