import keras
from keras import layers
from keras import ops

# Define Sequential model with 3 layers
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

[<Dense name=dense, built=False>,
 <Dense name=dense_1, built=False>,
 <Dense name=dense_2, built=False>]

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

model.pop()
print(len(model.layers))  # 2

2

model = keras.Sequential(name="my_sequential")
model.add(layers.Dense(2, activation="relu", name="layer1"))
model.add(layers.Dense(3, activation="relu", name="layer2"))
model.add(layers.Dense(4, name="layer3"))

layer = layers.Dense(3)
layer.weights  # Empty

[]

# Call layer on a test input
x = ops.ones((1, 4)) # creates a tensor of shape (1, 4) filled with ones.
y = layer(x)
layer.weights  # Now it has weights, of shape (4, 3) and (3,)

[<Variable path=dense_6/kernel, shape=(4, 3), dtype=float32, value=[[ 0.772823    0.559764   -0.4932681 ]
  [ 0.01619363 -0.34241778  0.6054685 ]
  [ 0.7129122   0.34047163 -0.08443046]
  [-0.6911168  -0.6273356   0.03513336]]>,
 <Variable path=dense_6/bias, shape=(3,), dtype=float32, value=[0. 0. 0.]>]

model = keras.Sequential(
    [
        #keras.Input(shape=(4,)), # if this removed, No weights defined at this stage!
        layers.Dense(2, activation="relu"),
        layers.Dense(3, activation="relu"),
        layers.Dense(4),
    ]
)
 
model.weights

[]

# Call the model on a test input
# performs a forward pass of the input x through the model.
x = ops.ones((1, 4))
y = model(x)
# Now we have weights becuase input shape is defined
model.weights

[<Variable path=sequential_11/dense_31/kernel, shape=(4, 2), dtype=float32, value=[[ 0.5186703  -0.89275837]
  [-0.32034516  0.22481966]
  [ 0.1135633  -0.88960075]
  [-0.47233725  0.6958945 ]]>,
 <Variable path=sequential_11/dense_31/bias, shape=(2,), dtype=float32, value=[0. 0.]>,
 <Variable path=sequential_11/dense_32/kernel, shape=(2, 3), dtype=float32, value=[[ 0.10157859  0.19588745  0.05911839]
  [ 0.4548638   0.4834206  -1.0363262 ]]>,
 <Variable path=sequential_11/dense_32/bias, shape=(3,), dtype=float32, value=[0. 0. 0.]>,
 <Variable path=sequential_11/dense_33/kernel, shape=(3, 4), dtype=float32, value=[[ 0.08667135  0.63437176  0.64811933 -0.4640239 ]
  [ 0.4898951  -0.38968176  0.40150666 -0.8447258 ]
  [-0.09795547  0.2846216  -0.6438199  -0.68790245]]>,
 <Variable path=sequential_11/dense_33/bias, shape=(4,), dtype=float32, value=[0. 0. 0. 0.]>]

y

<tf.Tensor: shape=(1, 4), dtype=float32, numpy=
array([[-0.06730284, -0.033949  , -0.05837943,  0.04649062]],
      dtype=float32)>

model.summary()

Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (1, 2)                      │              10 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (1, 3)                      │               9 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 (Dense)                      │ (1, 4)                      │              16 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 35 (140.00 B)
 Trainable params: 35 (140.00 B)
 Non-trainable params: 0 (0.00 B)

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

model.layers

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"))
 
model.summary()

Model: "sequential_12"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense_34 (Dense)                │ (None, 64)             │        50,240 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_35 (Dense)                │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_36 (Dense)                │ (None, 10)             │           650 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 55,050 (215.04 KB)
 Trainable params: 55,050 (215.04 KB)
 Non-trainable params: 0 (0.00 B)

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
 
# Preprocess the data (these are NumPy arrays)
x_train = x_train.reshape(60000, 784).astype("float32") / 255
x_test = x_test.reshape(10000, 784).astype("float32") / 255
 
y_train = y_train.astype("float32")
y_test = y_test.astype("float32")
 
# Reserve 10,000 samples for validation
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step

model.compile(
    optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),  # Optimizer
    # Loss function to minimize
    loss=keras.losses.SparseCategoricalCrossentropy(),
    # List of metrics to monitor
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)

model.compile(
    optimizer="rmsprop",
    loss="sparse_categorical_crossentropy",
    metrics=["sparse_categorical_accuracy"],
)

print("Fit model on training data")
history = model.fit(
    x_train,
    y_train,
    batch_size=64,
    epochs=2,
    # We pass some validation for
    # monitoring validation loss and metrics
    # at the end of each epoch
    validation_data=(x_val, y_val),
)

Fit model on training data
Epoch 1/2
782/782 ━━━━━━━━━━━━━━━━━━━━ 6s 5ms/step - loss: 0.5874 - sparse_categorical_accuracy: 0.8398 - val_loss: 0.1857 - val_sparse_categorical_accuracy: 0.9482
Epoch 2/2
782/782 ━━━━━━━━━━━━━━━━━━━━ 7s 3ms/step - loss: 0.1720 - sparse_categorical_accuracy: 0.9499 - val_loss: 0.1634 - val_sparse_categorical_accuracy: 0.9500

print(history.history)

# Evaluate the model on the test data using `evaluate`
print("Evaluate on test data")
results = model.evaluate(x_test, y_test, batch_size=128)
print("test loss, test acc:", results)
 
# Generate predictions (probabilities -- the output of the last layer)
# on new data using `predict`
print("Generate predictions for 3 samples")
predictions = model.predict(x_test[:3])
print("predictions shape:", predictions.shape)

inputs = keras.Input(shape=(784,))

# Just for demonstration purposes.
img_inputs = keras.Input(shape=(32, 32, 3))

inputs.shape

inputs.dtype

dense = layers.Dense(64, activation="relu")
x = dense(inputs)

x = layers.Dense(64, activation="relu")(x)
outputs = layers.Dense(10)(x)

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

model.summary()

keras.utils.plot_model(model, "my_first_model.png")

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