ageron · November 6, 2019 01:11 · Nov 6, 2019
diff --git a/keras_alexnet.py b/keras_alexnet.py
@@ -0,0 +1,48 @@
+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))