23 lines
No EOL
797 B
Python
23 lines
No EOL
797 B
Python
model = km.Sequential()
|
|
model.add(kl.Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28, 1), data_format="channels_last"))
|
|
model.add(kl.Conv2D(64, (3, 3), activation='relu'))
|
|
model.add(kl.MaxPooling2D(pool_size=(2, 2)))
|
|
model.add(kl.Dropout(0.25))
|
|
model.add(kl.Flatten())
|
|
model.add(kl.Dense(128, activation='relu'))
|
|
model.add(kl.Dropout(0.5))
|
|
model.add(kl.Dense(N_classes, activation='softmax'))
|
|
# Résumé
|
|
model.summary()
|
|
# Apprentissage
|
|
model.compile(loss="sparse_categorical_crossentropy",
|
|
optimizer=ko.Adadelta(),
|
|
metrics=['accuracy'])
|
|
ts=time.time()
|
|
model.fit(X_train_conv, Y_train,
|
|
batch_size=batch_size,
|
|
epochs=epochs,
|
|
verbose=1,
|
|
validation_data=(X_test_conv, Y_test))
|
|
te=time.time()
|
|
t_train_conv = te-ts |