13 KiB
13 KiB
Tensorflow / Keras -- A fast non-introduction
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* TOC {:toc}Top
This is a fast overview how to get an MNIST example running under TF Keras
If you just want to start with Tensorflow / Keras (especially if it is a scientific project), then you want to reconsider using Keras. In this case please check (& use) PyTorch.
Questions to David Rotermund
Data loader / Data generator
| keras.utils.Sequence | "Base object for fitting to a sequence of data, such as a dataset." |
| tf.keras.utils.to_categorical | "Converts a class vector (integers) to binary class matrix." |
Basic
from tensorflow import keras
import numpy as np
class DataGenerator(keras.utils.Sequence):
def __init__(
self,
train: bool = True,
size_of_batch: int = 32,
number_of_classes: int = 10,
do_shuffle: bool = True,
) -> None:
super(DataGenerator, self).__init__()
if train is True:
self.pattern_storage: np.ndarray = np.load("./train_pattern_storage.npy")
self.label_storage: np.ndarray = np.load("./train_label_storage.npy")
else:
self.pattern_storage = np.load("./test_pattern_storage.npy")
self.label_storage = np.load("./test_label_storage.npy")
self.pattern_storage = self.pattern_storage.astype(np.float32)
self.pattern_storage /= np.max(self.pattern_storage)
self.dimensions: tuple[int, int] = (
self.pattern_storage.shape[1],
self.pattern_storage.shape[2],
)
# How many pattern are there?
self.number_of_pattern: int = self.label_storage.shape[0]
self.size_of_batch: int = size_of_batch
self.number_of_classes: int = number_of_classes
self.do_shuffle: bool = do_shuffle
if self.pattern_storage.ndim == 3:
self.number_of_channel: int = 1
else:
self.number_of_channel = self.pattern_storage.shape[3]
self.available_indices: np.ndarray = np.arange(self.number_of_pattern)
self.on_epoch_end()
def on_epoch_end(self) -> None:
self.available_indices = np.arange(self.number_of_pattern)
if self.do_shuffle is True:
np.random.shuffle(self.available_indices)
def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray]:
selected_indices: np.ndarray = self.available_indices[
index * self.size_of_batch : (index + 1) * self.size_of_batch
]
image, target = self.__data_generation(selected_indices)
return image, target
def __data_generation(
self, list_of_indice: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
image = np.empty(
(self.size_of_batch, *self.dimensions, self.number_of_channel),
dtype=np.float32,
)
target = np.empty((self.size_of_batch), dtype=int)
for i in range(0, len(list_of_indice)):
if self.pattern_storage.ndim == 3:
image[i, :, :, 0] = self.pattern_storage[
self.available_indices[list_of_indice[i]], :, :
]
else:
image[i, :, :, :] = self.pattern_storage[
self.available_indices[list_of_indice[i]], :, :, :
]
target[i] = self.label_storage[self.available_indices[list_of_indice[i]]]
return image, keras.utils.to_categorical(
target, num_classes=self.number_of_classes
)
def __len__(self):
return int(np.floor(self.number_of_pattern / self.size_of_batch))
if __name__ == "__main__":
pass
With data augmentation
To the pre-processing chain self.data_augmentation you can add other preprocessing layers which are then applied to the input before given to the network.
from tensorflow import keras
import numpy as np
class DataGenerator(keras.utils.Sequence):
def __init__(
self,
train: bool = True,
size_of_batch: int = 32,
number_of_classes: int = 10,
do_shuffle: bool = True,
) -> None:
super(DataGenerator, self).__init__()
if train is True:
self.pattern_storage: np.ndarray = np.load("./train_pattern_storage.npy")
self.label_storage: np.ndarray = np.load("./train_label_storage.npy")
else:
self.pattern_storage = np.load("./test_pattern_storage.npy")
self.label_storage = np.load("./test_label_storage.npy")
self.pattern_storage = self.pattern_storage.astype(np.float32)
self.pattern_storage /= np.max(self.pattern_storage)
self.dimensions: tuple[int, int] = (
self.pattern_storage.shape[1],
self.pattern_storage.shape[2],
)
reduction: tuple[int, int] = (4, 4)
if train is True:
self.data_augmentation = keras.Sequential(
[
keras.layers.RandomCrop(
height=self.dimensions[0] - reduction[0],
width=self.dimensions[1] - reduction[1],
),
]
)
else:
self.data_augmentation = keras.Sequential(
[
keras.layers.CenterCrop(
height=self.dimensions[0] - reduction[0],
width=self.dimensions[1] - reduction[1],
),
]
)
# How many pattern are there?
self.number_of_pattern: int = self.label_storage.shape[0]
self.size_of_batch: int = size_of_batch
self.number_of_classes: int = number_of_classes
self.do_shuffle: bool = do_shuffle
if self.pattern_storage.ndim == 3:
self.number_of_channel: int = 1
else:
self.number_of_channel = self.pattern_storage.shape[3]
self.available_indices: np.ndarray = np.arange(self.number_of_pattern)
self.on_epoch_end()
def on_epoch_end(self) -> None:
self.available_indices = np.arange(self.number_of_pattern)
if self.do_shuffle is True:
np.random.shuffle(self.available_indices)
def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray]:
selected_indices: np.ndarray = self.available_indices[
index * self.size_of_batch : (index + 1) * self.size_of_batch
]
image, target = self.__data_generation(selected_indices)
return image, target
def __data_generation(
self, list_of_indice: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
image = np.empty(
(self.size_of_batch, *self.dimensions, self.number_of_channel),
dtype=np.float32,
)
target = np.empty((self.size_of_batch), dtype=int)
for i in range(0, len(list_of_indice)):
if self.pattern_storage.ndim == 3:
image[i, :, :, 0] = self.pattern_storage[
self.available_indices[list_of_indice[i]], :, :
]
else:
image[i, :, :, :] = self.pattern_storage[
self.available_indices[list_of_indice[i]], :, :, :
]
target[i] = self.label_storage[self.available_indices[list_of_indice[i]]]
image = self.data_augmentation(image)
return image, keras.utils.to_categorical(
target, num_classes=self.number_of_classes
)
def __len__(self):
return int(np.floor(self.number_of_pattern / self.size_of_batch))
if __name__ == "__main__":
pass
Train an example MNIST network
| tf.keras.backend.clear_session | "Resets all state generated by Keras." |
| tf.keras.Sequential | "Sequential groups a linear stack of layers into a tf.keras.Model." |
| network.add() | "Adds a layer instance on top of the layer stack." |
| tf.keras.layers.Conv2D | "2D convolution layer (e.g. spatial convolution over images)." |
| tf.keras.layers.MaxPool2D | "Max pooling operation for 2D spatial data." |
| tf.keras.layers.Flatten | "Flattens the input. Does not affect the batch size." |
| tf.keras.layers.Dense | "Just your regular densely-connected NN layer." |
| network.compile() | "Configures the model for training." |
| tf.keras.metrics.categorical_crossentropy | "Computes the categorical crossentropy loss." |
| tf.keras.optimizers.Adam | "Optimizer that implements the Adam algorithm." |
| network.fit() | Trains the model for a fixed number of epochs (iterations on a dataset). |
| network.summary() | "Prints a string summary of the network." |
| network.save() | "Saves the model to Tensorflow SavedModel or a single HDF5 file." |
Parameters for the layers:
padding: "One of "valid", "same" or "causal" (case-insensitive). "valid" means no padding. "same" results in padding with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. "causal" results in causal (dilated) convolutions, e.g. output[t] does not depend on input[t+1:]. " use_bias: "Boolean, whether the layer uses a bias vector." activation: "Activation function to use. If you don't specify anything, no activation is applied (see keras.activations)." data_format: " A string, one of channels_last (default) or channels_first."
from tensorflow import keras
from DataGenerator import DataGenerator
epoch_max: int = 50
number_of_classes: int = 10
size_of_batch_train: int = 100
train_data = DataGenerator(
train=True,
size_of_batch=size_of_batch_train,
number_of_classes=number_of_classes,
do_shuffle=True,
)
number_of_channels: int = train_data.number_of_channel
input_dimensions = train_data.dimensions
number_of_pattern_train = train_data.number_of_pattern
number_of_output_channels_conv1: int = 32
number_of_output_channels_conv2: int = 64
number_of_neurons_flatten1: int = 1024
kernel_size_conv1: tuple[int, int] = (5, 5)
kernel_size_pool1: tuple[int, int] = (2, 2)
kernel_size_conv2: tuple[int, int] = (5, 5)
kernel_size_pool2: tuple[int, int] = (2, 2)
stride_conv1: tuple[int, int] = (1, 1)
stride_pool1: tuple[int, int] = (2, 2)
stride_conv2: tuple[int, int] = (1, 1)
stride_pool2: tuple[int, int] = (2, 2)
keras.backend.clear_session()
network = keras.Sequential()
# Conv 1
network.add(
keras.layers.Conv2D(
number_of_output_channels_conv1,
kernel_size=kernel_size_conv1,
activation="relu",
input_shape=(input_dimensions[0], input_dimensions[1], number_of_channels),
padding="valid",
strides=stride_conv1,
data_format="channels_last",
use_bias=True,
)
)
# Pool 1
network.add(
keras.layers.MaxPooling2D(
pool_size=kernel_size_pool1,
padding="valid",
strides=stride_pool1,
data_format="channels_last",
)
)
# Conv 2
network.add(
keras.layers.Conv2D(
number_of_output_channels_conv2,
kernel_size=kernel_size_conv2,
activation="relu",
padding="valid",
strides=stride_conv2,
data_format="channels_last",
use_bias=True,
)
)
# Pool 2
network.add(
keras.layers.MaxPooling2D(
pool_size=kernel_size_pool2,
padding="valid",
strides=stride_pool2,
data_format="channels_last",
)
)
# Flatten
network.add(keras.layers.Flatten(data_format="channels_last"))
# Full layer
network.add(
keras.layers.Dense(number_of_neurons_flatten1, activation="relu", use_bias=True)
)
# Output layer
network.add(keras.layers.Dense(number_of_classes, activation="softmax"))
network.compile(
loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=["accuracy"],
)
for epoch_id in range(0, epoch_max):
print(f"Epoch: {epoch_id} of {epoch_max - 1}")
network.fit(x=train_data)
network.summary()
network.save("Model_" + str(epoch_id) + ".h5")
Test the example network performance