import keras
from keras import layers, ops

model = keras.Sequential([
    keras.Input(shape=(3, 1)),
    layers.Dense(2, activation="relu", name="layer1"),
    layers.Dense(3, activation="relu", name="layer2"),
    layers.Dense(4, name="layer3"),
])

model = keras.Sequential([
    layers.Dense(2, activation="relu"),
    layers.Dense(3, activation="relu"),
    layers.Dense(4),
])

model.layers  # قائمة من 3 طبقات Dense

model = keras.Sequential()
model.add(layers.Dense(2, activation="relu"))
model.add(layers.Dense(3, activation="relu"))
model.pop()  # يزيل الطبقة الأخيرة

layer = layers.Dense(3)
layer.weights  # []

x = ops.ones((1, 4))
y = layer(x)
layer.weights  # now shape (4,3) kernel and (3,) bias

model = keras.Sequential()
model.add(keras.Input(shape=(4,)))
model.add(layers.Dense(2, activation="relu"))
model.summary()

dense_10 (Dense)   (None, 2)    10
Total params: 10

model = keras.Sequential()
model.add(keras.Input(shape=(784,)))
model.add(layers.Dense(64, activation="relu"))
model.add(layers.Dense(64, activation="relu"))
model.add(layers.Dense(10, activation="softmax"))

dense_11 (Dense)  (None, 64)  50,240
dense_12 (Dense)  (None, 64)   4,160
dense_13 (Dense)  (None, 10)     650
Total params: 55,050 (215.04 KB)

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

x_train = x_train.reshape(60000, 784).astype("float32") / 255
x_test = x_test.reshape(10000, 784).astype("float32") / 255

# Reserve 10,000 samples for validation
x_val, y_val = x_train[-10000:], y_train[-10000:]
x_train, y_train = x_train[:-10000], y_train[:-10000]

model.compile(
    optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),
    loss=keras.losses.SparseCategoricalCrossentropy(),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
# Shortcut with string identifiers:
# model.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy",
#               metrics=["sparse_categorical_accuracy"])

history = model.fit(
    x_train, y_train,
    batch_size=64,
    epochs=2,
    validation_data=(x_val, y_val),
)

Epoch 1/2 - loss: 0.5733 - sparse_categorical_accuracy: 0.8410 - val_loss: 0.3036 - val_sparse_categorical_accuracy: 0.9113
Epoch 2/2 - loss: 0.1803 - sparse_categorical_accuracy: 0.9460 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9594

results = model.evaluate(x_test, y_test, batch_size=128)
print("test loss, test acc:", results)
# test loss, test acc: [0.147, 0.956]

predictions = model.predict(x_test[:3])
print("predictions shape:", predictions.shape)  # (3, 10)

inputs = keras.Input(shape=(784,))          # input node

dense = layers.Dense(64, activation="relu")
x = dense(inputs)                            # "layer call" = drawing an arrow
x = layers.Dense(64, activation="relu")(x)
outputs = layers.Dense(10)(x)

model = keras.Model(inputs=inputs, outputs=outputs, name="mnist_model")

model.summary()
# input_layer_4 (InputLayer)  (None, 784)      0
# dense_14 (Dense)            (None, 64)  50,240
# dense_15 (Dense)            (None, 64)   4,160
# dense_16 (Dense)            (None, 10)     650

keras.utils.plot_model(model, "my_first_model.png")
keras.utils.plot_model(model, "my_first_model_with_shape_info.png", show_shapes=True)