def cifar10_test():
dtype_1 = np.complex64
(train_images, train_labels), (test_images,
test_labels) = datasets.cifar10.load_data()
# Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0
train_images = train_images.astype(dtype_1)
test_images = test_images.astype(dtype_1)
train_labels = train_labels.astype(dtype_1)
test_labels = test_labels.astype(dtype_1)
model = models.Sequential()
model.add(layers.ComplexInput(input_shape=(32, 32, 3),
dtype=dtype_1)) # Never forget this!!!
model.add(layers.ComplexConv2D(32, (3, 3), activation='cart_relu'))
model.add(layers.ComplexMaxPooling2D(
(2, 2))) # TODO: This is changing the dtype!
model.add(layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
model.add(layers.ComplexAvgPooling2D((2, 2)))
model.add(layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
model.add(layers.ComplexFlatten())
model.add(layers.ComplexDense(64, activation='cart_relu'))
model.add(layers.ComplexDense(10, activation='convert_to_real_with_abs'))
model.summary()
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
history = model.fit(train_images,
train_labels,
epochs=2,
validation_data=(test_images, test_labels))
def cifar10_test_model_1(train_images,
train_labels,
test_images,
test_labels,
dtype_1='complex64'):
model = models.Sequential()
model.add(layers.ComplexInput(input_shape=(32, 32, 3),
dtype=dtype_1)) # Never forget this!!!
model.add(layers.ComplexConv2D(32, (3, 3), activation='cart_relu'))
model.add(layers.ComplexMaxPooling2D((2, 2)))
model.add(layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
model.add(layers.ComplexAvgPooling2D((2, 2)))
model.add(layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
model.add(layers.ComplexFlatten())
model.add(layers.ComplexDense(64, activation='cart_relu'))
model.add(layers.ComplexDense(10, activation='convert_to_real_with_abs'))
model.summary()
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
return model.fit(train_images,
train_labels,
epochs=2,
validation_data=(test_images, test_labels),
shuffle=False)
def cifar10_test_model_2(train_images,
train_labels,
test_images,
test_labels,
dtype_1='complex64'):
x = layers.complex_input(shape=(32, 32, 3), dtype=dtype_1)
conv1 = layers.ComplexConv2D(32, (3, 3), activation='cart_relu')(x)
pool1 = layers.ComplexMaxPooling2D((2, 2))(conv1)
conv2 = layers.ComplexConv2D(64, (3, 3), activation='cart_relu')(pool1)
pool2 = layers.ComplexAvgPooling2D((2, 2))(conv2)
conv3 = layers.ComplexConv2D(64, (3, 3), activation='cart_relu')(pool2)
flat = layers.ComplexFlatten()(conv3)
dense1 = layers.ComplexDense(64, activation='cart_relu')(flat)
y = layers.ComplexDense(10, activation='convert_to_real_with_abs')(dense1)
model = models.Model(inputs=[x], outputs=[y])
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.summary()
return model.fit(train_images,
train_labels,
epochs=2,
validation_data=(test_images, test_labels),
shuffle=False)
def all_layers_model():
"""
Creates a model using all possible layers to assert no layer changes the dtype to real.
"""
input_shape = (4, 28, 28, 3)
x = tf.cast(tf.random.normal(input_shape), tf.complex64)
model = tf.keras.models.Sequential()
model.add(complex_layers.ComplexInput(
input_shape=input_shape[1:])) # Always use ComplexInput at the start
model.add(complex_layers.ComplexConv2D(32, (3, 3), activation='cart_relu'))
model.add(complex_layers.ComplexAvgPooling2D((2, 2)))
model.add(
complex_layers.ComplexConv2D(64, (3, 3), activation='cart_sigmoid'))
model.add(complex_layers.ComplexDropout(0.5))
model.add(complex_layers.ComplexMaxPooling2D((2, 2)))
model.add(complex_layers.ComplexConv2DTranspose(32, (2, 2)))
model.add(complex_layers.ComplexFlatten())
model.add(complex_layers.ComplexDense(64, activation='cart_tanh'))
model.compile(loss=tf.keras.losses.MeanAbsoluteError(),
optimizer='adam',
metrics=['accuracy'])
y = model(x)
assert y.dtype == np.complex64
return model
def test_cifar():
(train_images, train_labels), (test_images, test_labels) = get_dataset()
# Create your model
model = tf.keras.models.Sequential()
model.add(complex_layers.ComplexInput(input_shape=(32, 32, 3))) # Always use ComplexInput at the start
model.add(complex_layers.ComplexConv2D(32, (3, 3), activation='cart_relu'))
model.add(complex_layers.ComplexAvgPooling2D((2, 2)))
model.add(complex_layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
model.add(complex_layers.ComplexFlatten())
model.add(complex_layers.ComplexDense(64, activation='cart_relu'))
model.add(complex_layers.ComplexDense(10, activation='convert_to_real_with_abs'))
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# model.summary()
history = model.fit(train_images, train_labels, epochs=1, validation_data=(test_images, test_labels))
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)