###############################################################################
## The DL (Deep Learning) Hello World Program
## References:
## https://www.tensorflow.org/tutorials/
## https://medium.com/the-andela-way/deep-learning-hello-world-e1fc53ea888
###############################################################################
import tensorflow as tf
from keras.datasets import mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten( ),
tf.keras.layers.Dense (512 , activation = tf.nn.relu ),
tf.keras.layers.Dropout(0.2 ),
tf.keras.layers.Dense (10 , activation = tf.nn.softmax)
])
model.compile(optimizer = 'adam' ,
loss = 'sparse_categorical_crossentropy',
metrics = ['accuracy'] )
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
###############################################################################
# Output
###############################################################################
# Using TensorFlow backend.
# Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
# 11493376/11490434 [==============================] - 1s 0us/step
# Epoch 1/5
# 60000/60000 [==============================] - 14s 236us/step - loss: 0.2027 - acc: 0.9406
# Epoch 2/5
# 60000/60000 [==============================] - 14s 225us/step - loss: 0.0805 - acc: 0.9756
# Epoch 3/5
# 60000/60000 [==============================] - 13s 222us/step - loss: 0.0517 - acc: 0.9839
# Epoch 4/5
# 60000/60000 [==============================] - 14s 227us/step - loss: 0.0370 - acc: 0.9883
# Epoch 5/5
# 60000/60000 [==============================] - 13s 224us/step - loss: 0.0262 - acc: 0.9917
# 10000/10000 [==============================] - 1s 51us/step
# [0.07304962697861483, 0.9789]
Thursday 17 January 2019
Deep Learning Hello World Program
Wednesday 16 January 2019
TensorFlow Keras Models
compile(optimizer ,
loss = None,
metrics = None,
loss_weights = None,
sample_weight_mode = None,
weighted_metrics = None,
target_tensors = None)
fit(x = None,
y = None,
batch_size = None,
epochs = 1 ,
verbose = 1 ,
callbacks = None,
validation_split = 0.0 ,
validation_data = None,
shuffle = True,
class_weight = None,
sample_weight = None,
initial_epoch = 0 ,
steps_per_epoch = None,
validation_steps = None)
evaluate(x = None,
y = None,
batch_size = None,
verbose = 1 ,
sample_weight = None,
steps = None)
predict(x,
batch_size = None,
verbose = 0 ,
steps = None)
train_on_batch(x, y, sample_weight=None, class_weight=None)
test_on_batch (x, y, sample_weight=None)
predict_on_batch(x)
fit_generator(generator ,
steps_per_epoch = None ,
epochs = 1 ,
verbose = 1 ,
callbacks = None ,
validation_data = None ,
validation_steps = None ,
class_weight = None ,
max_queue_size = 10 ,
workers = 1 ,
use_multiprocessing = False,
shuffle = True ,
initial_epoch = 0 )
evaluate_generator(generator ,
steps = None ,
max_queue_size = 10 ,
workers = 1 ,
use_multiprocessing = False,
verbose = 0 )
predict_generator(generator ,
steps = None ,
max_queue_size = 10 ,
workers = 1 ,
use_multiprocessing = False,
verbose = 0 )
get_layer(name=None, index=None)
Reference: keras.io
TensorFlow Keras Constraints
keras.constraints.MaxNorm(max_value=2, axis=0)
keras.constraints.NonNeg()
keras.constraints.UnitNorm(axis=0)
keras.constraints.MinMaxNorm(min_value=0.0, max_value=1.0, rate=1.0, axis=0)
Reference: keras.io
TensorFlow Keras Regulizers
keras.regularizers.l1 (0.)
keras.regularizers.l2 (0.)
keras.regularizers.l1_l2(l1 = 0.01, l2 = 0.01)
Reference: keras.io
TensorFlow Keras Initializers
keras.initializers.Initializer()
keras.initializers.Zeros()
keras.initializers.Ones()
keras.initializers.Constant(value=0)
keras.initializers.RandomNormal (mean = 0.0 , stddev = 0.05, seed = None)
keras.initializers.RandomUniform (minval = -0.05, maxval = 0.05, seed = None)
keras.initializers.TruncatedNormal(mean = 0.0 , stddev = 0.05, seed = None)
keras.initializers.VarianceScaling(scale = 1.0 ,
mode = 'fan_in',
distribution = 'normal',
seed = None )
keras.initializers.Orthogonal(gain=1.0, seed=None)
keras.initializers.Identity (gain=1.0)
keras.initializers.lecun_uniform (seed=None)
keras.initializers.glorot_normal (seed=None)
keras.initializers.glorot_uniform(seed=None)
keras.initializers.he_normal (seed=None)
keras.initializers.lecun_normal (seed=None)
keras.initializers.he_uniform (seed=None)
Reference: keras.io
TensorFlow Keras Applications
keras.applications.xception.Xception(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.vgg16.VGG16(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.vgg19.VGG19(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.resnet50.ResNet50(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.inception_v3.InceptionV3(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.inception_resnet_v2.InceptionResNetV2(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.mobilenet.MobileNet(input_shape = None,
alpha = 1.0,
depth_multiplier = 1,
dropout = 1e-3,
include_top = True,
weights = 'imagenet',
input_tensor = None,
pooling = None,
classes = 1000)
keras.applications.densenet.DenseNet121(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.densenet.DenseNet169(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.densenet.DenseNet201(include_top = True ,
weights = 'imagenet',
input_tensor = None ,
input_shape = None ,
pooling = None ,
classes = 1000 )
keras.applications.nasnet.NASNetLarge(input_shape = None ,
include_top = True ,
weights = 'imagenet',
input_tensor = None ,
pooling = None ,
classes = 1000 )
keras.applications.nasnet.NASNetMobile(input_shape = None ,
include_top = True ,
weights = 'imagenet',
input_tensor = None ,
pooling = None ,
classes = 1000 )
keras.applications.mobilenet_v2.MobileNetV2(input_shape = None ,
alpha = 1.0 ,
depth_multiplier = 1 ,
include_top = True ,
weights = 'imagenet',
input_tensor = None ,
pooling = None ,
classes = 1000 )
Reference: keras.io
TensorFlow Keras Callback Functions
keras.callbacks.BaseLogger(stateful_metrics=None)
keras.callbacks.TerminateOnNaN()
keras.callbacks.ProgbarLogger(count_mode='samples', stateful_metrics=None)
keras.callbacks.History()
keras.callbacks.ModelCheckpoint(filepath ,
monitor = 'val_loss',
verbose = 0 ,
save_best_only = False ,
save_weights_only = False ,
mode = 'auto' ,
period = 1 )
keras.callbacks.EarlyStopping(monitor = 'val_loss',
min_delta = 0 ,
patience = 0 ,
verbose = 0 ,
mode = 'auto' ,
baseline = None ,
restore_best_weights = False )
keras.callbacks.RemoteMonitor(root = 'http://localhost:9000',
path = '/publish/epoch/end/' ,
field = 'data' ,
headers = None ,
send_as_json = False )
keras.callbacks.LearningRateScheduler(schedule, verbose=0)
keras.callbacks.TensorBoard(log_dir = './logs',
histogram_freq = 0 ,
batch_size = 32 ,
write_graph = True ,
write_grads = False ,
write_images = False ,
embeddings_freq = 0 ,
embeddings_layer_names = None ,
embeddings_metadata = None ,
embeddings_data = None ,
update_freq = 'epoch' )
keras.callbacks.ReduceLROnPlateau(monitor = 'val_loss',
factor = 0.1 ,
patience = 10 ,
verbose = 0 ,
mode = 'auto' ,
min_delta = 0.0001 ,
cooldown = 0 ,
min_lr = 0 )
keras.callbacks.CSVLogger(filename, separator=',', append=False)
keras.callbacks.LambdaCallback(on_epoch_begin = None, on_epoch_end = None,
on_batch_begin = None, on_batch_end = None,
on_train_begin = None, on_train_end = None)
Reference: keras.io
TensorFlow Keras Activation Functions
keras.activations.softmax (x, axis = -1 )
keras.activations.elu (x, alpha = 1.0)
keras.activations.selu (x)
keras.activations.softplus (x)
keras.activations.softsign (x)
keras.activations.relu (x, alpha = 0.0, max_value=None, threshold=0.0)
keras.activations.tanh (x)
keras.activations.sigmoid (x)
keras.activations.hard_sigmoid(x)
keras.activations.exponential (x)
keras.activations.linear (x)
Reference: keras.io
TensorFlow Keras Optimizers
keras.optimizers.SGD (lr = 0.01 , momentum=0.0, decay=0.0, nesterov=False)
keras.optimizers.RMSprop (lr = 0.001, rho = 0.9 , epsilon=None, decay=0.0)
keras.optimizers.Adagrad (lr = 0.01 , epsilon=None, decay=0.0)
keras.optimizers.Adadelta(lr = 1.0 , rho = 0.95 , epsilon=None, decay=0.0)
keras.optimizers.Adam(lr = 0.001,
beta_1 = 0.9 ,
beta_2 = 0.999,
epsilon = None ,
decay = 0.0 ,
amsgrad = False)
keras.optimizers.Adamax(lr = 0.002,
beta_1 = 0.9 ,
beta_2 = 0.999,
epsilon = None ,
decay = 0.0 )
keras.optimizers.Nadam(lr = 0.002,
beta_1 = 0.9 ,
beta_2 = 0.999,
epsilon = None ,
schedule_decay = 0.004)
Reference: keras.io
TensorFlow Keras Metrics
keras.metrics.binary_accuracy (y_true, y_pred )
keras.metrics.categorical_accuracy (y_true, y_pred )
keras.metrics.sparse_categorical_accuracy (y_true, y_pred )
keras.metrics.top_k_categorical_accuracy (y_true, y_pred, k = 5)
keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k = 5)
Reference: keras.io
TensorFlow Keras Utilities
keras.utils.to_categorical(y, num_classes=None, dtype='float32')
keras.utils.normalize(x, axis=-1, order=2)
keras.utils.get_file(fname ,
origin ,
untar = False ,
md5_hash = None ,
file_hash = None ,
cache_subdir = 'datasets',
hash_algorithm = 'auto' ,
extract = False ,
archive_format = 'auto' ,
cache_dir = None )
keras.utils.print_summary(model ,
line_length = None,
positions = None,
print_fn = None)
keras.utils.plot_model(model ,
to_file = 'model.png',
show_shapes = False ,
show_layer_names = True ,
rankdir = 'TB' ,
expand_nested = False ,
dpi = 96 )
Reference: keras.io
TensorFlow Keras Loss Functions
keras.losses.mean_squared_error (y_true, y_pred)
keras.losses.mean_absolute_error (y_true, y_pred)
keras.losses.mean_absolute_percentage_error (y_true, y_pred)
keras.losses.mean_squared_logarithmic_error (y_true, y_pred)
keras.losses.squared_hinge (y_true, y_pred)
keras.losses.hinge (y_true, y_pred)
keras.losses.categorical_hinge (y_true, y_pred)
keras.losses.logcosh (y_true, y_pred)
keras.losses.categorical_crossentropy (y_true, y_pred)
keras.losses.sparse_categorical_crossentropy(y_true, y_pred)
keras.losses.binary_crossentropy (y_true, y_pred)
keras.losses.kullback_leibler_divergence (y_true, y_pred)
keras.losses.poisson (y_true, y_pred)
keras.losses.cosine_proximity (y_true, y_pred)
Reference: keras.io
TensorFlow Keras Layers
####
model = Sequential()
model.add(Dense(32, input_shape=(16,)))
model.add(Conv2D(64 ,
(3, 3) ,
input_shape = (3, 32, 32),
padding = 'same', ))
model.add(LSTM(32))
####
keras.engine.input_layer.Input()
keras.layers.Dense(units ,
activation = None ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
bias_constraint = None )
keras.layers.Activation(activation)
keras.layers.Dropout(rate, noise_shape=None, seed=None)
keras.layers.Flatten(data_format=None)
keras.layers.Reshape(target_shape)
keras.layers.Permute(dims)
keras.layers.RepeatVector(n)
keras.layers.Lambda(function, output_shape=None, mask=None, arguments=None)
keras.layers.ActivityRegularization(l1=0.0, l2=0.0)
keras.layers.Masking(mask_value=0.0)
keras.layers.SpatialDropout1D(rate)
keras.layers.SpatialDropout2D(rate, data_format=None)
keras.layers.SpatialDropout3D(rate, data_format=None)
keras.layers.Conv1D(filters ,
kernel_size ,
strides = 1 ,
padding = 'valid' ,
data_format = 'channels_last' ,
dilation_rate = 1 ,
activation = None ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
bias_constraint = None )
keras.layers.Conv2D(filters ,
kernel_size ,
strides = (1, 1) ,
padding = 'valid' ,
data_format = None ,
dilation_rate = (1, 1) ,
activation = None ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
bias_constraint = None )
keras.layers.SeparableConv1D(filters,
kernel_size ,
strides = 1 ,
padding = 'valid' ,
data_format = 'channels_last' ,
dilation_rate = 1 ,
depth_multiplier = 1 ,
activation = None ,
use_bias = True ,
depthwise_initializer = 'glorot_uniform',
pointwise_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
depthwise_regularizer = None ,
pointwise_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
depthwise_constraint = None ,
pointwise_constraint = None ,
bias_constraint = None )
keras.layers.SeparableConv2D(filters,
kernel_size ,
strides = (1, 1) ,
padding = 'valid' ,
data_format = None ,
dilation_rate = (1, 1) ,
depth_multiplier = 1 ,
activation = None ,
use_bias = True ,
depthwise_initializer = 'glorot_uniform',
pointwise_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
depthwise_regularizer = None ,
pointwise_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
depthwise_constraint = None ,
pointwise_constraint = None ,
bias_constraint = None )
keras.layers.DepthwiseConv2D(kernel_size ,
strides = (1, 1) ,
padding = 'valid' ,
depth_multiplier = 1 ,
data_format = None ,
activation = None ,
use_bias = True ,
depthwise_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
depthwise_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
depthwise_constraint = None ,
bias_constraint = None )
keras.layers.Conv2DTranspose(filters ,
kernel_size ,
strides = (1, 1) ,
padding = 'valid' ,
output_padding = None ,
data_format = None ,
dilation_rate = (1, 1) ,
activation = None ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
bias_constraint = None )
keras.layers.Conv3D(filters,
kernel_size ,
strides = (1, 1, 1) ,
padding = 'valid' ,
data_format = None ,
dilation_rate = (1, 1, 1) ,
activation = None ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
bias_constraint = None )
keras.layers.Conv3DTranspose(filters,
kernel_size ,
strides = (1, 1, 1) ,
padding = 'valid' ,
output_padding = None ,
data_format = None ,
activation = None ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
bias_constraint = None )
keras.layers.Cropping1D(cropping=(1, 1) )
keras.layers.Cropping2D(cropping=((0, 0), (0, 0) ), data_format=None)
keras.layers.Cropping3D(cropping=((1, 1), (1, 1), (1, 1)), data_format=None)
keras.layers.UpSampling1D(size=2)
keras.layers.UpSampling2D(size = (2, 2) ,
data_format = None ,
interpolation = 'nearest')
keras.layers.UpSampling3D(size=(2, 2, 2), data_format=None)
keras.layers.ZeroPadding1D(padding=1)
keras.layers.ZeroPadding2D(padding=(1, 1), data_format=None)
keras.layers.ZeroPadding3D(padding=(1, 1, 1), data_format=None)
keras.layers.MaxPooling1D(pool_size = 2 ,
strides = None ,
padding = 'valid' ,
data_format = 'channels_last')
keras.layers.MaxPooling2D(pool_size = (2, 2) ,
strides = None ,
padding = 'valid',
data_format = None )
keras.layers.MaxPooling3D(pool_size = (2, 2, 2),
strides = None ,
padding = 'valid' ,
data_format = None )
keras.layers.AveragePooling1D(pool_size = 2 ,
strides = None ,
padding = 'valid' ,
data_format = 'channels_last')
keras.layers.AveragePooling2D(pool_size = (2, 2) ,
strides = None ,
padding = 'valid',
data_format = None )
keras.layers.AveragePooling3D(pool_size = (2, 2, 2),
strides = None ,
padding = 'valid' ,
data_format = None )
keras.layers.GlobalMaxPooling1D (data_format = 'channels_last')
keras.layers.GlobalAveragePooling1D(data_format = 'channels_last')
keras.layers.GlobalMaxPooling2D (data_format = None )
keras.layers.GlobalAveragePooling2D(data_format = None )
keras.layers.GlobalMaxPooling3D (data_format = None )
keras.layers.GlobalAveragePooling3D(data_format = None )
keras.layers.LocallyConnected1D(filters ,
kernel_size ,
strides = 1 ,
padding = 'valid' ,
data_format = None ,
activation = None ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
bias_constraint = None )
keras.layers.LocallyConnected2D(filters ,
kernel_size ,
strides = (1, 1) ,
padding = 'valid' ,
data_format = None ,
activation = None ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
bias_constraint = None )
keras.layers.RNN(cell ,
return_sequences = False,
return_state = False,
go_backwards = False,
stateful = False,
unroll = False)
keras.layers.SimpleRNN(units ,
activation = 'tanh' ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
dropout = 0.0 ,
recurrent_dropout = 0.0 ,
return_sequences = False ,
return_state = False ,
go_backwards = False ,
stateful = False ,
unroll = False )
keras.layers.GRU(units ,
activation = 'tanh' ,
recurrent_activation = 'hard_sigmoid' ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
dropout = 0.0 ,
recurrent_dropout = 0.0 ,
implementation = 1 ,
return_sequences = False ,
return_state = False ,
go_backwards = False ,
stateful = False ,
unroll = False ,
reset_after = False )
keras.layers.LSTM(units ,
activation = 'tanh' ,
recurrent_activation = 'hard_sigmoid' ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
unit_forget_bias = True ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
dropout = 0.0 ,
recurrent_dropout = 0.0 ,
implementation = 1 ,
return_sequences = False ,
return_state = False ,
go_backwards = False ,
stateful = False ,
unroll = False )
keras.layers.ConvLSTM2D(filters ,
kernel_size ,
strides = (1, 1) ,
padding = 'valid' ,
data_format = None ,
dilation_rate = (1, 1) ,
activation = 'tanh' ,
recurrent_activation = 'hard_sigmoid' ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
unit_forget_bias = True ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
return_sequences = False ,
go_backwards = False ,
stateful = False ,
dropout = 0.0 ,
recurrent_dropout = 0.0 )
keras.layers.SimpleRNNCell(units ,
activation = 'tanh' ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
dropout = 0.0 ,
recurrent_dropout = 0.0 )
keras.layers.GRUCell(units ,
activation = 'tanh' ,
recurrent_activation = 'hard_sigmoid' ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
dropout = 0.0 ,
recurrent_dropout = 0.0 ,
implementation = 1 ,
reset_after = False )
keras.layers.LSTMCell(units ,
activation = 'tanh' ,
recurrent_activation = 'hard_sigmoid' ,
use_bias = True ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
unit_forget_bias = True ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
dropout = 0.0 ,
recurrent_dropout = 0.0 ,
implementation = 1 )
keras.layers.CuDNNGRU(units ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
return_sequences = False ,
return_state = False ,
stateful = False )
keras.layers.CuDNNLSTM(units ,
kernel_initializer = 'glorot_uniform',
recurrent_initializer = 'orthogonal' ,
bias_initializer = 'zeros' ,
unit_forget_bias = True ,
kernel_regularizer = None ,
recurrent_regularizer = None ,
bias_regularizer = None ,
activity_regularizer = None ,
kernel_constraint = None ,
recurrent_constraint = None ,
bias_constraint = None ,
return_sequences = False ,
return_state = False ,
stateful = False )
keras.layers.Embedding(input_dim ,
output_dim ,
embeddings_initializer = 'uniform',
embeddings_regularizer = None ,
activity_regularizer = None ,
embeddings_constraint = None ,
mask_zero = False ,
input_length = None )
keras.layers.LeakyReLU(alpha=0.3)
keras.layers.PReLU(alpha_initializer = 'zeros',
alpha_regularizer = None ,
alpha_constraint = None ,
shared_axes = None )
keras.layers.ELU (alpha = 1.0 )
keras.layers.ThresholdedReLU(theta = 1.0 )
keras.layers.Softmax (axis = -1 )
keras.layers.ReLU (max_value = None,negative_slope=0.0,threshold=0.0)
keras.layers.BatchNormalization(axis = -1 ,
momentum = 0.99 ,
epsilon = 0.001 ,
center = True ,
scale = True ,
beta_initializer = 'zeros',
gamma_initializer = 'ones' ,
moving_mean_initializer = 'zeros',
moving_variance_initializer = 'ones' ,
beta_regularizer = None ,
gamma_regularizer = None ,
beta_constraint = None ,
gamma_constraint = None )
keras.layers.GaussianNoise (stddev)
keras.layers.GaussianDropout(rate )
keras.layers.AlphaDropout (rate , noise_shape=None, seed=None)
keras.layers.TimeDistributed(layer )
####
keras.layers.Add ()
keras.layers.Subtract()
keras.layers.Multiply()
keras.layers.Average ()
keras.layers.Maximum ()
keras.layers.Concatenate(axis = -1)
keras.layers.Dot (axes , normalize=False)
####
keras.layers.add (inputs)
keras.layers.subtract (inputs)
keras.layers.multiply (inputs)
keras.layers.average (inputs)
keras.layers.maximum (inputs)
keras.layers.concatenate(inputs, axis = -1)
keras.layers.dot (inputs, axes , normalize = False)
####
layer.get_weights()
layer.set_weights(weights)
layer.get_config()
layer.input
layer.output
layer.input_shape
layer.output_shape
layer.get_input_at (node_index)
layer.get_output_at (node_index)
layer.get_input_shape_at (node_index)
layer.get_output_shape_at(node_index)
Reference: keras.io
TensorFlow Keras Input
####
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
####
fashion_mnist = keras.datasets.fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
####
imdb = keras.datasets.imdb
(train_data, train_labels),
(test_data , test_labels ) = imdb.load_data(num_words=10000)
####
dataset_path = keras.utils.get_file(
"auto-mpg.data",
"https://archive.ics.uci.edu/ml/" +
"machine-learning-databases/auto-mpg/auto-mpg.data")
column_names = ['MPG' , 'Cylinders' , 'Displacement', 'Horsepower',
'Weight', 'Acceleration', 'Model Year' , 'Origin' ]
raw_dataset = pd.read_csv(dataset_path , names = column_names,
na_values = "?", comment = '\t' ,
sep = " ", skipinitialspace = True )
####
from keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
####
from keras.datasets import cifar100
(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine')
####
from keras.datasets import reuters
(x_train, y_train), (x_test, y_test) = reuters.load_data(
path = "reuters.npz",
num_words = None ,
skip_top = 0 ,
maxlen = None ,
test_split = 0.2 ,
seed = 113 ,
start_char = 1 ,
oov_char = 2 ,
index_from = 3 )
####
from keras.datasets import boston_housing
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
Reference: keras.io
Sunday 13 January 2019
Tensor Broadcasting, Adding, Multiplying, Reshaping
Paste the code below (but not the output section) into a Colab (https:\\colab.research.google.com\) Jupyter Notebook,if you would like to execute it.
-------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------
# Broadcasting - enlarging a tensor to match another one, but repeating data.
###############################################################################
import numpy as np
x1 = np.array([1,2,3])
print('x1: '); print (x1)
print('x1.ndim: ', x1.ndim)
print('x1.shape: ', x1.shape)
x2 = np.array([[1,2,3],[4,5,6]])
print('x2: ') ; print (x2)
print('x2.ndim: ', x2.ndim)
print('x2.shape: ', x2.shape)
x3 = x1 + x2
print('x3: '); print(x3)
print('x3.ndim: ', x3.ndim)
print('x3.shape: ', x3.shape)
# Vector Dot Product
###############################################################################
v1 = np.array([1,2,3])
v2 = np.array([1,2,3])
v3 = v1.dot(v2)
print('v3: ', v3)
# Matrix - Vector Multiplication
###############################################################################
m1 = np.array([[1,2,3],[4,5,6],[7,8,9]])
v1 = np.array([10,11,12])
v2 = m1.dot(v1)
print('v2: ', v2)
# Tensor Multiplication
###############################################################################
t1 = np.array([[1,2,3],[4,5,6],[7,8,9]])
t2 = np.array([[10,11,12],[13,14,15],[16,17,18]])
t3 = t1.dot(t2)
print('t3: '); print(t3)
# Reshaping Tensors
###############################################################################
print("t3.shape: ", t3.shape)
t4 = t3.reshape(1,9)
print("t4"); print(t4)
print("t4.shape: ", t4.shape)
t5 = t4.reshape(9,)
print("t5"); print(t5)
print("t5.shape: ", t5.shape)
t6 = t4.reshape(9,1)
print("t6"); print(t6)
print("t6.shape: ", t6.shape)
-------------------------------------------------------------------------------
-- Output
-------------------------------------------------------------------------------
1:
[1 2 3]
x1.ndim: 1
x1.shape: (3,)
x2:
[[1 2 3]
[4 5 6]]
x2.ndim: 2
x2.shape: (2, 3)
x3:
[[2 4 6]
[5 7 9]]
x3.ndim: 2
x3.shape: (2, 3)
v3: 14
v2: [ 68 167 266]
t3:
[[ 84 90 96]
[201 216 231]
[318 342 366]]
t3.shape: (3, 3)
t4
[[ 84 90 96 201 216 231 318 342 366]]
t4.shape: (1, 9)
t5
[ 84 90 96 201 216 231 318 342 366]
t5.shape: (9,)
t6
[[ 84]
[ 90]
[ 96]
[201]
[216]
[231]
[318]
[342]
[366]]
t6.shape: (9, 1)
Wednesday 9 January 2019
NumPy Scalars, Vectors, Matrices, Tensors Examples
Paste the code below (but not the output section) into a Colab (https:\\colab.research.google.com\) Jupyter Notebook,if you would like to execute it.
-------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------
import numpy as np
# Numpy uses array() to create objects of ndarray type.
###########################################
# Vectors
# 0-Dimensions (aka the origin of a Vector Space)
v0 = np.array(0) # also [0.], [0., 0.], [0., 0., 0.], ...
print('v0 : ', v0 )
print('v0.shape: ', v0.shape)
print('v0.ndim : ', v0.ndim )
# 1-Dimension (aka Scalar)
v1 = np.array([1.])
print('v1 : ', v1 )
print('v1.shape: ', v1.shape)
print('v1.ndim : ', v1.ndim )
# 2-Dimensions
v2 = np.array([1., 2.])
print('v2 : ', v2 )
print('v2.shape: ', v2.shape)
print('v2.ndim : ', v2.ndim )
# 3-Dimensions
v3 = np.array([1., 2., 3.])
print('v3.shape: ', v3.shape)
print('v3.ndim : ', v3.ndim )
print('v3 : ', v3 )
# 4-Dimensions
v4 = np.array([1., 2., 3., 4.])
print('v4 : ', v4 )
print('v4.shape: ', v4.shape)
print('v4.ndim : ', v4.ndim )
###########################################
# Matrices
# 0-Dimensions (aka Scalar)
m0 = np.array(4.)
print('m0 : ', m0 )
print('m0.shape: ', m0.shape)
print('m0.ndim : ', m0.ndim )
# 1-Dimension (aka Scalar (if one coordinate),
# aka Vector (if more than one coordinate))
m1 = np.array([1.])
print('m1 : ', m1 )
print('m1.shape: ', m1.shape)
print('m1.ndim : ', m1.ndim )
# 2-Dimensions
m2 = np.array([[1., 2.],
[3., 4.]])
print('m2 : ', m2 )
print('m2.shape: ', m2.shape)
print('m2.ndim : ', m2.ndim )
# 3-Dimensions
m3 = np.array([[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]])
print('m3 : ', m3 )
print('m3.shape: ', m3.shape)
print('m3.ndim : ', m3.ndim )
# 4-Dimensions
m4 = np.array([[ 1., 2., 3., 4.],
[ 5., 6., 7., 8.],
[ 9., 10., 11., 12.],
[13., 14., 15., 16.]])
print('m4 : ', m4 )
print('m4.shape: ', m4.shape)
print('m4.ndim : ', m4.ndim )
# Tensors
###########################################
# 0-Dimensions (aka Scalar)
t0 = np.array(4.)
print('t0 : ', t0 )
print('t0.shape: ', t0.shape)
print('t0.ndim : ', t0.ndim )
# 1-Dimension
t1 = np.array([1., 2., 3.])
print('t1 : ', t1 )
print('t1.shape: ', t1.shape)
print('t1.ndim : ', t1.ndim )
# 2-Dimensions
t2 = np.array([[1., 2.],
[3., 4.]])
print('t2 : ', t2 )
print('t2.shape: ', t2.shape)
print('t2.ndim : ', t2.ndim )
# 3-Dimensions
t3 = np.array([[[ 1., 2., 3.],
[ 4., 5., 6.],
[ 7., 8., 9.]],
[[10., 11., 12.],
[13., 14., 15.],
[16., 17., 18.]],
[[19., 20., 21.],
[22., 23., 24.],
[25., 26., 27.]]])
print('t3 : ', t3 )
print('t3.shape: ', t3.shape)
print('t3.ndim : ', t3.ndim )
# 4-Dimensions
t4 = np.array([[[[ 1., 2., 3., 4.],
[ 5., 6., 7., 8.],
[ 9., 10., 11., 12.],
[ 13., 14., 15., 16.]],
[[ 17., 18., 19., 20.],
[ 21., 22., 23., 24.],
[ 25., 26., 27., 28.],
[ 29., 30., 31., 32.]],
[[ 33., 34., 35., 36.],
[ 37., 38., 39., 40.],
[ 41., 42., 43., 44.],
[ 45., 46., 47., 48.]],
[[ 49., 50., 51., 52.],
[ 53., 54., 55., 56.],
[ 57., 58., 59., 60.],
[ 61., 62., 63., 64.]]],
[[[ 65., 66., 67., 68.],
[ 69., 70., 71., 72.],
[ 73., 74., 75., 76.],
[ 77., 78., 79., 80.]],
[[ 81., 82., 83., 84.],
[ 85., 86., 87., 88.],
[ 89., 90., 91., 92.],
[ 93., 94., 95., 96.]],
[[ 97., 98., 99., 100.],
[101., 102., 103., 104.],
[105., 106., 107., 108.],
[109., 110., 111., 112.]],
[[113., 114., 115., 116.],
[117., 118., 119., 120.],
[121., 122., 123., 124.],
[125., 126., 127., 128.]]],
[[[129., 130., 131., 132.],
[133., 134., 135., 136.],
[137., 138., 139., 140.],
[141., 142., 143., 144.]],
[[145., 146., 147., 148.],
[149., 150., 151., 152.],
[153., 154., 155., 156.],
[157., 158., 159., 160.]],
[[161., 162., 163., 164.],
[165., 166., 167., 168.],
[169., 170., 171., 172.],
[173., 174., 175., 176.]],
[[177., 178., 179., 180.],
[181., 182., 183., 184.],
[185., 186., 187., 188.],
[189., 190., 191., 192.]]],
[[[193., 194., 195., 196.],
[197., 198., 199., 200.],
[201., 202., 203., 204.],
[205., 206., 207., 208.]],
[[209., 210., 211., 212.],
[213., 214., 215., 216.],
[217., 218., 219., 220.],
[221., 222., 223., 224.]],
[[225., 226., 227., 228.],
[229., 230., 231., 232.],
[233., 234., 235., 236.],
[237., 238., 239., 240.]],
[[241., 242., 243., 244.],
[245., 246., 247., 248.],
[249., 250., 251., 252.],
[253., 254., 255., 256.]]]])
print('t4 : ', t4 )
print('t4.shape: ', t4.shape)
print('t4.ndim : ', t4.ndim )
# Note how the data structures are like: Javascript, Perl, and JSON.
------------------------------------------------------------------------------------------------------
Output
------------------------------------------------------------------------------------------------------
v0 : 0
v0.shape: ()
v0.ndim : 0
v1 : [1.]
v1.shape: (1,)
v1.ndim : 1
v2 : [1. 2.]
v2.shape: (2,)
v2.ndim : 1
v3.shape: (3,)
v3.ndim : 1
v3 : [1. 2. 3.]
v4 : [1. 2. 3. 4.]
v4.shape: (4,)
v4.ndim : 1
m0 : 4.0
m0.shape: ()
m0.ndim : 0
m1 : [1.]
m1.shape: (1,)
m1.ndim : 1
m2 : [[1. 2.]
[3. 4.]]
m2.shape: (2, 2)
m2.ndim : 2
m3 : [[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]]
m3.shape: (3, 3)
m3.ndim : 2
m4 : [[ 1. 2. 3. 4.]
[ 5. 6. 7. 8.]
[ 9. 10. 11. 12.]
[13. 14. 15. 16.]]
m4.shape: (4, 4)
m4.ndim : 2
t0 : 4.0
t0.shape: ()
t0.ndim : 0
t1 : [1. 2. 3.]
t1.shape: (3,)
t1.ndim : 1
t2 : [[1. 2.]
[3. 4.]]
t2.shape: (2, 2)
t2.ndim : 2
t3 : [[[ 1. 2. 3.]
[ 4. 5. 6.]
[ 7. 8. 9.]]
[[10. 11. 12.]
[13. 14. 15.]
[16. 17. 18.]]
[[19. 20. 21.]
[22. 23. 24.]
[25. 26. 27.]]]
t3.shape: (3, 3, 3)
t3.ndim : 3
t4 : [[[[ 1. 2. 3. 4.]
[ 5. 6. 7. 8.]
[ 9. 10. 11. 12.]
[ 13. 14. 15. 16.]]
[[ 17. 18. 19. 20.]
[ 21. 22. 23. 24.]
[ 25. 26. 27. 28.]
[ 29. 30. 31. 32.]]
[[ 33. 34. 35. 36.]
[ 37. 38. 39. 40.]
[ 41. 42. 43. 44.]
[ 45. 46. 47. 48.]]
[[ 49. 50. 51. 52.]
[ 53. 54. 55. 56.]
[ 57. 58. 59. 60.]
[ 61. 62. 63. 64.]]]
[[[ 65. 66. 67. 68.]
[ 69. 70. 71. 72.]
[ 73. 74. 75. 76.]
[ 77. 78. 79. 80.]]
[[ 81. 82. 83. 84.]
[ 85. 86. 87. 88.]
[ 89. 90. 91. 92.]
[ 93. 94. 95. 96.]]
[[ 97. 98. 99. 100.]
[101. 102. 103. 104.]
[105. 106. 107. 108.]
[109. 110. 111. 112.]]
[[113. 114. 115. 116.]
[117. 118. 119. 120.]
[121. 122. 123. 124.]
[125. 126. 127. 128.]]]
[[[129. 130. 131. 132.]
[133. 134. 135. 136.]
[137. 138. 139. 140.]
[141. 142. 143. 144.]]
[[145. 146. 147. 148.]
[149. 150. 151. 152.]
[153. 154. 155. 156.]
[157. 158. 159. 160.]]
[[161. 162. 163. 164.]
[165. 166. 167. 168.]
[169. 170. 171. 172.]
[173. 174. 175. 176.]]
[[177. 178. 179. 180.]
[181. 182. 183. 184.]
[185. 186. 187. 188.]
[189. 190. 191. 192.]]]
[[[193. 194. 195. 196.]
[197. 198. 199. 200.]
[201. 202. 203. 204.]
[205. 206. 207. 208.]]
[[209. 210. 211. 212.]
[213. 214. 215. 216.]
[217. 218. 219. 220.]
[221. 222. 223. 224.]]
[[225. 226. 227. 228.]
[229. 230. 231. 232.]
[233. 234. 235. 236.]
[237. 238. 239. 240.]]
[[241. 242. 243. 244.]
[245. 246. 247. 248.]
[249. 250. 251. 252.]
[253. 254. 255. 256.]]]]
t4.shape: (4, 4, 4, 4)
t4.ndim : 4
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