keras_alexnet.py

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D
from tensorflow.keras.layers import MaxPooling2D, BatchNormalization

keras.backend.clear_session()
np.random.seed(1000)
tf.random.set_seed(1000)

model = Sequential([
  Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), padding="same",
         activation="relu", input_shape=(224,224,3)),
  MaxPooling2D(pool_size=(3,3), strides=(2,2), padding="valid"),

  Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), padding="same",
         activation="relu"),
  MaxPooling2D(pool_size=(3,3), strides=(2,2), padding="valid"),

  Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding="same",
         activation="relu"),

  Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding="same",
         activation="relu"),

  Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding="same",
        activation="relu"),
  MaxPooling2D(pool_size=(3,3), strides=(2,2), padding="valid"),

  Flatten(),

  Dense(4096, activation="relu"),
  Dropout(0.4),

  Dense(4096, activation="relu"),
  Dropout(0.4),

  Dense(1000, activation="softmax")
])

model.summary()

model.compile(loss="categorical_crossentropy", optimizer="adam",
              metrics=["accuracy"])
              
#model.fit(X_train, y_train, epochs=10,
#          validation_data=(X_valid, y_valid))