使用keras搭神經網路久了，想整理一下keras新增網路層的n種方法，以防日後忘記。
本文以mnist手寫資料為例，神經網路結構使用lenet講解。
以下是模組的匯入，資料的載入，資料的預處理。
import keras
from keras.datasets import mnist
from keras.layers import conv2d, maxpool2d, activation, dropout, flatten, dense
from keras.losses import categorical_crossentropy
from keras.models import model, sequential, input
from keras.optimizers import sgd
from keras.utils import to_categorical
import numpy as np
(x_train, y_train)
,(x_test, y_test)
= mnist.load_data(
)x_train = np.expand_dims(x_train,3)
x_test = np.expand_dims(x_test,3)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = sequential(
)model.add(conv2d(20,
(3,3
), input_shape=
(x_train.shape[1]
, x_train.shape[2]
, x_train.shape[3]
)))model.add(maxpool2d((2
,2))
)model.add(activation(
'tanh'))
model.add(conv2d(30,
(3,3
)))model.add(maxpool2d((2
,2))
)model.add(activation(
'tanh'))
model.add(dropout(
0.5)
)model.add(flatten())
model.add(dense(
1000))
model.add(dropout(
0.5)
)model.add(dense(y_train.shape[1]
))model.add(activation(
'softmax'))
model.
compile
(optimizer=sgd(learning_rate=
0.01
, momentum=
0.9, nesterov=
true
), loss=categorical_crossentropy, metrics=
['accuracy'])
model.fit(x_train, y_train, batch_size=
1000
, epochs=
10)
model = sequential(
[ conv2d(20,
(3,3
), input_shape=
(x_train.shape[1]
, x_train.shape[2]
, x_train.shape[3]
)), maxpool2d((2
,2))
, activation(
'tanh'),
conv2d(30,
(3,3
)), maxpool2d((2
,2))
, activation(
'tanh'),
dropout(
0.5)
, flatten(),
dense(
1000),
dropout(
0.5)
, dense(y_train.shape[1]
),activation(
'softmax'),
])model.
compile
(optimizer=sgd(learning_rate=
0.01
, momentum=
0.9, nesterov=
true
), loss=categorical_crossentropy, metrics=
['accuracy'])
model.fit(x_train, y_train, batch_size=
1000
, epochs=
10)
x_i = input(
(x_train.shape[1]
, x_train.shape[2]
, x_train.shape[3]
))x = conv2d(20,
(3,3
))(x_i)
x = maxpool2d((2
,2))
(x)x = activation(
'tanh'
)(x)
x = conv2d(30,
(3,3
))(x)x = maxpool2d((2
,2))
(x)x = activation(
'tanh'
)(x)
x = dropout(
0.5)
(x)x = flatten(
)(x)
x = dense(
1000
)(x)
x = dropout(
0.5)
(x)x = dense(y_train.shape[1]
)(x)
pred = activation(
'softmax'
)(x)
model = model(inputs=x_i, outputs=pred)
model.
compile
(optimizer=sgd(learning_rate=
0.01
, momentum=
0.9, nesterov=
true
), loss=categorical_crossentropy, metrics=
['accuracy'])
model.fit(x_train, y_train, batch_size=
1000
, epochs=
10)
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