Keras 简明教程

Python 3.6.5 (v3.6.5:f59c0932b4, Mar 28 2018, 17:00:18)
[MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>>

python3 -m venv kerasenv

py -m venv keras

$ cd kerasvenv kerasvenv $ source bin/activate

.\env\Scripts\activate

pip install numpy

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pip install pandas

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pandas-3.1.1-cp36-cp36m-macosx_10_6_intel.
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pip install matplotlib

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pip install scipy

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pip install -U scikit-learn

pip install seaborn

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Successfully installed cycler-0.10.0 kiwisolver-1.1.0
matplotlib-3.1.1 pandas-0.25.0 pyparsing-2.4.2
python-dateutil-2.8.0 pytz-2019.2 seaborn-0.9.0

deactivate

conda create --name PythonCPU

activate PythonCPU

conda install spyder

conda install -c anaconda <module-name>

conda install -c anaconda pandas

conda install -c anaconda keras

spyder

{
   "image_data_format": "channels_last",
   "epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow"
}

> cd home
> mkdir .keras
> vi keras.json

{
   "image_data_format": "channels_last",
   "epsilon": 1e-07,
   "floatx": "float32",
   "backend": "theano"
}

>>> import keras as k
using theano backend.

>>> k.get_uid(prefix='')
1
>>> k.get_uid(prefix='') 2

>>> k.reset_uids()

>>> k.get_uid(prefix='')
1

>>> data = k.placeholder(shape = (1,3,3))
>>> data
<tf.Tensor 'Placeholder_9:0' shape = (1, 3, 3) dtype = float32>

If you use int_shape(), it will show the shape.

>>> k.int_shape(data) (1, 3, 3)

>>> a = k.placeholder(shape = (4,2))
>>> b = k.placeholder(shape = (2,3))
>>> c = k.dot(a,b)
>>> c
<tf.Tensor 'MatMul_3:0' shape = (4, 3) dtype = float32>
>>>

>>> res = k.ones(shape = (2,2))

#print the value

>>> k.eval(res)
array([[1., 1.], [1., 1.]], dtype = float32)

>>> a_batch = k.ones(shape = (2,3))
>>> b_batch = k.ones(shape = (3,2))
>>> c_batch = k.batch_dot(a_batch,b_batch)
>>> c_batch
<tf.Tensor 'ExpandDims:0' shape = (2, 1) dtype = float32>

>>> data = k.variable([[10,20,30,40],[50,60,70,80]])
#variable initialized here
>>> result = k.transpose(data)
>>> print(result)
Tensor("transpose_6:0", shape = (4, 2), dtype = float32)
>>> print(k.eval(result))
   [[10. 50.]
   [20. 60.]
   [30. 70.]
   [40. 80.]]

>>> data = np.array([[10,20,30,40],[50,60,70,80]])

>>> print(np.transpose(data))
   [[10 50]
   [20 60]
   [30 70]
   [40 80]]

>>> res = k.variable(value = data)
>>> print(res)
<tf.Variable 'Variable_7:0' shape = (2, 4) dtype = float32_ref>

>>> a = k.placeholder((2, 2), sparse=True)

>>> print(a) SparseTensor(indices =
   Tensor("Placeholder_8:0",
   shape = (?, 2), dtype = int64),
values = Tensor("Placeholder_7:0", shape = (?,),
dtype = float32), dense_shape = Tensor("Const:0", shape = (2,), dtype = int64))

>>> print(k.is_sparse(a)) True

>>> b = k.to_dense(a)
>>> print(b) Tensor("SparseToDense:0", shape = (2, 2), dtype = float32)
>>> print(k.is_sparse(b)) False

k.random_uniform_variable(shape, mean, scale)

>>> a = k.random_uniform_variable(shape = (2, 3), low=0, high = 1)
>>> b = k. random_uniform_variable(shape = (3,2), low = 0, high = 1)
>>> c = k.dot(a, b)
>>> k.int_shape(c)
(2, 2)

from keras.utils import HDF5Matrix data = HDF5Matrix('data.hdf5', 'data')

>>> from keras.utils import to_categorical
>>> labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> to_categorical(labels)
array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
   [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
   [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
   [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
   [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
   [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
   [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],
   [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],
   [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],
   [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]], dtype = float32)
>>> from keras.utils import normalize
>>> normalize([1, 2, 3, 4, 5])
array([[0.13483997, 0.26967994, 0.40451992, 0.53935989, 0.67419986]])

from keras.utils import print_summary print_summary(model)

from keras.utils import plot_model
plot_model(model,to_file = 'image.png')

>>> import numpy as np
>>> shape = (4, 2)
>>> input = np.zeros(shape)
>>> print(input)
[
   [0. 0.]
   [0. 0.]
   [0. 0.]
   [0. 0.]
]
>>>

>>> import numpy as np
>>> shape = (3, 4, 2)
>>> input = np.zeros(shape)
>>> print(input)
[
   [[0. 0.] [0. 0.] [0. 0.] [0. 0.]]
   [[0. 0.] [0. 0.] [0. 0.] [0. 0.]]
   [[0. 0.] [0. 0.] [0. 0.] [0. 0.]]
]
>>>

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.Zeros()
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.Ones()
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.Constant(value = 0) model.add(
   Dense(512, activation = 'relu', input_shape = (784,), kernel_initializer = my_init)
)

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.RandomNormal(mean=0.0,
stddev = 0.05, seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

from keras import initializers

my_init = initializers.RandomUniform(minval = -0.05, maxval = 0.05, seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.TruncatedNormal(mean = 0.0, stddev = 0.05, seed = None
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.VarianceScaling(
   scale = 1.0, mode = 'fan_in', distribution = 'normal', seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   skernel_initializer = my_init))

stddev = sqrt(scale / n)

limit = sqrt(3 * scale / n)

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.RandomUniform(minval = -0.05, maxval = 0.05, seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

stddev = sqrt(1 / fan_in)

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.lecun_uniform(seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

limit = sqrt(3 / fan_in)

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.glorot_normal(seed=None) model.add(
   Dense(512, activation = 'relu', input_shape = (784,), kernel_initializer = my_init)
)

stddev = sqrt(2 / (fan_in + fan_out))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.glorot_uniform(seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

limit = sqrt(6 / (fan_in + fan_out))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.RandomUniform(minval = -0.05, maxval = 0.05, seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

stddev = sqrt(2 / fan_in)

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.he_normal(seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

limit = sqrt(6 / fan_in)

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.Orthogonal(gain = 1.0, seed = None)
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.Identity(gain = 1.0) model.add(
   Dense(512, activation = 'relu', input_shape = (784,), kernel_initializer = my_init)
)

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import initializers

my_init = initializers.Identity(gain = 1.0) model.add(
   Dense(512, activation = 'relu', input_shape = (784,),
   kernel_initializer = my_init)
)

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import constraints

my_constrain = constraints.UnitNorm(axis = 0)
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_constraint = my_constrain))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import constraints

my_constrain = constraints.MaxNorm(max_value = 2, axis = 0)
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_constraint = my_constrain))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import constraints

my_constrain = constraints.MinMaxNorm(min_value = 0.0, max_value = 1.0, rate = 1.0, axis = 0)
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_constraint = my_constrain))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import regularizers

my_regularizer = regularizers.l1(0.)
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_regularizer = my_regularizer))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import regularizers

my_regularizer = regularizers.l2(0.)
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_regularizer = my_regularizer))

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import regularizers

my_regularizer = regularizers.l2(0.)
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,),
   kernel_regularizer = my_regularizer))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'linear', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'elu', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'selu', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'softmax', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'softplus', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'softsign', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense
model = Sequential()
model.add(Dense(512, activation = 'tanh', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'sigmoid', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'hard_sigmoid', input_shape = (784,)))

from keras.models import Sequential
from keras.layers import Activation, Dense

model = Sequential()
model.add(Dense(512, activation = 'exponential', input_shape = (784,)))

from keras.models import Sequential

model = Sequential()
input_layer = Dense(32, input_shape=(8,)) model.add(input_layer)
hidden_layer = Dense(64, activation='relu'); model.add(hidden_layer)
output_layer = Dense(8)
model.add(output_layer)

>>> layers = model.layers
>>> layers
[
   <keras.layers.core.Dense object at 0x000002C8C888B8D0>,
   <keras.layers.core.Dense object at 0x000002C8C888B7B8>
   <keras.layers.core.Dense object at 0x 000002C8C888B898>
]

>>> inputs = model.inputs
>>> inputs
[<tf.Tensor 'dense_13_input:0' shape=(?, 8) dtype=float32>]

>>> outputs = model.outputs
>>> outputs
<tf.Tensor 'dense_15/BiasAdd:0' shape=(?, 8) dtype=float32>]

config = model.get_config()

new_model = Sequential.from_config(config)

>>> json_string = model.to_json()
>>> json_string '{"class_name": "Sequential", "config":
{"name": "sequential_10", "layers":
[{"class_name": "Dense", "config":
{"name": "dense_13", "trainable": true, "batch_input_shape":
[null, 8], "dtype": "float32", "units": 32, "activation": "linear",
"use_bias": true, "kernel_initializer":
{"class_name": "Vari anceScaling", "config":
{"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}},
"bias_initializer": {"class_name": "Zeros", "conf
ig": {}}, "kernel_regularizer": null, "bias_regularizer": null,
"activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}},
{" class_name": "Dense", "config": {"name": "dense_14", "trainable": true,
"dtype": "float32", "units": 64, "activation": "relu", "use_bias": true,
"kern el_initializer": {"class_name": "VarianceScaling", "config":
{"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}},
"bias_initia lizer": {"class_name": "Zeros",
"config": {}}, "kernel_regularizer": null, "bias_regularizer": null,
"activity_regularizer": null, "kernel_constraint" : null, "bias_constraint": null}},
{"class_name": "Dense", "config": {"name": "dense_15", "trainable": true,
"dtype": "float32", "units": 8, "activation": "linear", "use_bias": true,
"kernel_initializer": {"class_name": "VarianceScaling", "config":
{"scale": 1.0, "mode": "fan_avg", "distribution": " uniform", "seed": null}},
"bias_initializer": {"class_name": "Zeros", "config": {}},
"kernel_regularizer": null, "bias_regularizer": null, "activity_r egularizer":
null, "kernel_constraint": null, "bias_constraint":
null}}]}, "keras_version": "2.2.5", "backend": "tensorflow"}'
>>>

from keras.models import model_from_json
new_model = model_from_json(json_string)

>>> yaml_string = model.to_yaml()
>>> yaml_string 'backend: tensorflow\nclass_name:
Sequential\nconfig:\n layers:\n - class_name: Dense\n config:\n
activation: linear\n activity_regular izer: null\n batch_input_shape:
!!python/tuple\n - null\n - 8\n bias_constraint: null\n bias_initializer:\n
class_name : Zeros\n config: {}\n bias_regularizer: null\n dtype:
float32\n kernel_constraint: null\n
kernel_initializer:\n cla ss_name: VarianceScaling\n config:\n
distribution: uniform\n mode: fan_avg\n
scale: 1.0\n seed: null\n kernel_regularizer: null\n name: dense_13\n
trainable: true\n units: 32\n
use_bias: true\n - class_name: Dense\n config:\n activation: relu\n activity_regularizer: null\n
bias_constraint: null\n bias_initializer:\n class_name: Zeros\n
config : {}\n bias_regularizer: null\n dtype: float32\n
kernel_constraint: null\n kernel_initializer:\n class_name: VarianceScalin g\n
config:\n distribution: uniform\n mode: fan_avg\n scale: 1.0\n
seed: null\n kernel_regularizer: nu ll\n name: dense_14\n trainable: true\n
units: 64\n use_bias: true\n - class_name: Dense\n config:\n
activation: linear\n activity_regularizer: null\n
bias_constraint: null\n bias_initializer:\n
class_name: Zeros\n config: {}\n bias_regu larizer: null\n
dtype: float32\n kernel_constraint: null\n
kernel_initializer:\n class_name: VarianceScaling\n config:\n
distribution: uniform\n mode: fan_avg\n
scale: 1.0\n seed: null\n kernel_regularizer: null\n name: dense _15\n
trainable: true\n units: 8\n
use_bias: true\n name: sequential_10\nkeras_version: 2.2.5\n'
>>>

from keras.models import model_from_yaml
new_model = model_from_yaml(yaml_string)

>>> model.summary() Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param
#================================================================
dense_13 (Dense) (None, 32) 288
_________________________________________________________________
dense_14 (Dense) (None, 64) 2112
_________________________________________________________________
dense_15 (Dense) (None, 8) 520
=================================================================
Total params: 2,920
Trainable params: 2,920
Non-trainable params: 0
_________________________________________________________________
>>>

>>> from keras.layers import Input

>>> data = Input(shape=(2,3))

>>> from keras.layers import Dense

>>> layer = Dense(2)(data)
>>> print(layer)
Tensor("dense_1/add:0", shape =(?, 2, 2), dtype = float32)

from keras.models import Model

model = Model(inputs = data, outputs = layer)

from keras.layers import Input
from keras.models import Model
from keras.layers import Dense

data = Input(shape=(2,3))
layer = Dense(2)(data) model =
Model(inputs=data,outputs=layer) model.summary()
_________________________________________________________________
Layer (type)               Output Shape               Param #
=================================================================
input_2 (InputLayer)       (None, 2, 3)               0
_________________________________________________________________
dense_2 (Dense)            (None, 2, 2)               8
=================================================================
Total params: 8
Trainable params: 8
Non-trainable params: 0
_________________________________________________________________

import numpy as np

x_train = np.random.random((100,4,8))
y_train = np.random.random((100,10))

x_val = np.random.random((100,4,8))
y_val = np.random.random((100,10))

from keras.models import Sequential model = Sequential()

from keras.layers import LSTM, Dense

# add a sequence of vectors of dimension 16
model.add(LSTM(16, return_sequences = True))
model.add(Dense(10, activation = 'softmax'))

model.compile(
   loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = ['accuracy']
)

model.fit(x_train, y_train, batch_size = 32, epochs = 5, validation_data = (x_val, y_val))

from keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import numpy as np

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation = 'softmax'))

model.compile(loss = 'categorical_crossentropy',
   optimizer = RMSprop(),
   metrics = ['accuracy'])

history = model.fit(
   x_train, y_train,
   batch_size = 128,
   epochs = 20,
   verbose = 1,
   validation_data = (x_test, y_test)
)