def dense_example():
img_r = np.array([[
[0, 1, 2],
[0, 2, 2],
[0, 5, 7]
], [
[0, 4, 5],
[3, 7, 9],
[4, 5, 3]
]]).astype(np.float32)
img_i = np.array([[
[0, 4, 5],
[3, 7, 9],
[4, 5, 3]
], [
[0, 4, 5],
[3, 7, 9],
[4, 5, 3]
]]).astype(np.float32)
img = img_r + 1j * img_i
c_flat = ComplexFlatten()
c_dense = ComplexDense(units=10)
res = c_dense(c_flat(img.astype(np.complex64)))
assert res.shape == [2, 10]
assert res.dtype == tf.complex64
model = tf.keras.models.Sequential()
model.add(ComplexInput(input_shape=(3, 3)))
model.add(ComplexFlatten())
model.add(ComplexDense(32, activation='cart_relu'))
model.add(ComplexDense(32))
assert model.output_shape == (None, 32)
res = model(img.astype(np.complex64))
def fashion_mnist_example():
dtype_1 = np.complex64
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images,
test_labels) = fashion_mnist.load_data()
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 = tf.keras.Sequential([
ComplexInput(input_shape=(28, 28)),
ComplexFlatten(),
ComplexDense(128,
activation='cart_relu',
kernel_initializer=ComplexGlorotUniform(seed=0)),
ComplexDense(10,
activation='convert_to_real_with_abs',
kernel_initializer=ComplexGlorotUniform(seed=0))
])
model.summary()
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=2)
def serial_layers():
model = Sequential()
model.add(ComplexDense(32, activation='relu', input_shape=(32, 32, 3)))
model.add(ComplexDense(32))
print(model.output_shape)
img_r = np.array([[
[0, 1, 2],
[0, 2, 2],
[0, 5, 7]
], [
[0, 4, 5],
[3, 7, 9],
[4, 5, 3]
]]).astype(np.float32)
img_i = np.array([[
[0, 4, 5],
[3, 7, 9],
[4, 5, 3]
], [
[0, 4, 5],
[3, 7, 9],
[4, 5, 3]
]]).astype(np.float32)
img = img_r + 1j * img_i
model = Sequential()
# model.add(ComplexInput(img.shape[1:]))
model.add(ComplexFlatten(input_shape=img.shape[1:]))
model.add(ComplexDense(units=10))
res = model(img)
def mnist_example():
(ds_train, ds_test), ds_info = tfds.load(
'mnist',
split=['train', 'test'],
shuffle_files=True,
as_supervised=True,
with_info=True,
)
ds_train = ds_train.map(normalize_img,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds_train = ds_train.cache()
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
ds_train = ds_train.batch(128)
ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)
ds_test = ds_test.map(normalize_img,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds_test = ds_test.batch(128)
ds_test = ds_test.cache()
ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)
model = tf.keras.models.Sequential([
ComplexFlatten(input_shape=(28, 28, 1)),
ComplexDense(128, activation='relu', dtype=tf.float32),
ComplexDense(10, dtype=tf.float32)
])
model.compile(
optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)
model.fit(
ds_train,
epochs=2,
validation_data=ds_test,
)
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import datasets
from cvnn.layers import ComplexDense, ComplexFlatten
from pdb import set_trace
(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
train_images, test_images = tf.cast(train_images, tf.complex64) / 255.0, tf.cast(test_images, tf.complex64) / 255.0
model = Sequential([
ComplexFlatten(input_shape=(28, 28, 1)),
ComplexDense(128, activation='relu', input_shape=(28, 28, 1)),
ComplexDense(10, activation='softmax')
])
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=Adam(0.001),
metrics=['accuracy'],
)
print(model.predict(train_images[:10]).dtype)
# model.fit(
# train_images, train_labels,
# epochs=6,
# validation_data=(test_images, test_labels),
# )