Yet Another Deep Learning Framework.
import quantumbrain as qb
from quantumbrain.datasets import mnist
import numpy as np
# resnet block
def res_block(name, inputs, filters, down_sample=False):
strides = 2 if down_sample else 1
layer = qb.layers.Conv(filters, 1, 1, kernel_initializer="he", name=name+"conv:1")(inputs)
layer = qb.layers.Relu(name=name+"relu:1")(layer)
layer = qb.layers.Conv(filters, 3, strides, kernel_initializer="he", name=name+"conv:2")(layer)
layer = qb.layers.Relu(name=name+"relu:2")(layer)
shortcut = inputs
if down_sample:
shortcut = qb.layers.Conv(filters, 1, strides, name=name+"conv:3")(shortcut)
layer = qb.layers.Add(name=name+"add:1")([layer, shortcut])
layer = qb.layers.Relu(name=name+"relu:3")(layer)
return layer
def create_model():
inputs = qb.layers.Input([None, 1, 28, 28])
x = qb.layers.Conv(6, 3, 2, kernel_initializer="he")(inputs)
x = qb.layers.Relu()(x)
x = res_block("block1_0", x, 6, True)
for idx in range(2):
x = res_block("block1_{}".format(1 + idx), x, 6, False)
x = qb.layers.Dropout(drop_rate=0.5)(x)
x = qb.layers.Flatten()(x)
x = qb.layers.Dense(100, kernel_initializer="he")(x)
x = qb.layers.Relu()(x)
x = qb.layers.Dense(50, kernel_initializer="he")(x)
x = qb.layers.Relu()(x)
output = qb.layers.Dense(10)(x)
model = qb.model.Model(inputs=inputs, outputs=output)
return model
def accuracy(x, y):
model.trainable = False
y_pred = model(x)
y_pred = np.argmax(y_pred, axis=1)
if y.ndim != 1:
y = np.argmax(y, axis=1)
return np.sum(y_pred == y) / x.shape[0]
model = create_model()
model.summary()
loss = qb.losses.CE()
optimizer = qb.optimizers.Adam()
trainer = qb.trainer.Trainer(model, loss, optimizer)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
batch_size = 100
for epoch in range(10):
for batch in range(0, len(x_train), batch_size):
x_batch = x_train[batch:batch+batch_size]
y_batch = y_train[batch:batch+batch_size]
pred_value, loss_value = trainer.train(x_batch, y_batch)
acc = accuracy(x_test, y_test)
print("epoch: {}, accuracy: {}".format(epoch, acc))
if epoch == 9:
model.save("./result/ckpt_{}".format(epoch))Output:
Model: "model"
----------------------------------------------------------
Layer(type) Output Shape
==========================================================
Input_0(Input) [None, 1, 28, 28]
---------------------------------------------------------
Conv_1(Conv) [None, 6, 14, 14]
---------------------------------------------------------
Relu_2(Relu) [None, 6, 14, 14]
---------------------------------------------------------
block1_0conv:1(Conv) [None, 6, 14, 14]
---------------------------------------------------------
block1_0relu:1(Relu) [None, 6, 14, 14]
---------------------------------------------------------
block1_0conv:2(Conv) [None, 6, 7, 7]
---------------------------------------------------------
block1_0relu:2(Relu) [None, 6, 7, 7]
---------------------------------------------------------
block1_0conv:3(Conv) [None, 6, 7, 7]
---------------------------------------------------------
block1_0add:1(Add) [None, 6, 7, 7]
---------------------------------------------------------
block1_0relu:3(Relu) [None, 6, 7, 7]
---------------------------------------------------------
block1_1conv:1(Conv) [None, 6, 7, 7]
---------------------------------------------------------
block1_1relu:1(Relu) [None, 6, 7, 7]
---------------------------------------------------------
block1_1conv:2(Conv) [None, 6, 7, 7]
---------------------------------------------------------
block1_1relu:2(Relu) [None, 6, 7, 7]
---------------------------------------------------------
block1_1add:1(Add) [None, 6, 7, 7]
---------------------------------------------------------
block1_1relu:3(Relu) [None, 6, 7, 7]
---------------------------------------------------------
block1_2conv:1(Conv) [None, 6, 7, 7]
---------------------------------------------------------
block1_2relu:1(Relu) [None, 6, 7, 7]
---------------------------------------------------------
block1_2conv:2(Conv) [None, 6, 7, 7]
---------------------------------------------------------
block1_2relu:2(Relu) [None, 6, 7, 7]
---------------------------------------------------------
block1_2add:1(Add) [None, 6, 7, 7]
---------------------------------------------------------
block1_2relu:3(Relu) [None, 6, 7, 7]
---------------------------------------------------------
Dropout_22(Dropout) [None, 6, 7, 7]
---------------------------------------------------------
Flatten_23(Flatten) [None, 294]
---------------------------------------------------------
Dense_24(Dense) [None, 100]
---------------------------------------------------------
Relu_25(Relu) [None, 100]
---------------------------------------------------------
Dense_26(Dense) [None, 50]
---------------------------------------------------------
Relu_27(Relu) [None, 50]
---------------------------------------------------------
Dense_28(Dense) [None, 10]
==========================================================
epoch: 0, accuracy: 0.8924
epoch: 1, accuracy: 0.9162
epoch: 2, accuracy: 0.9378
epoch: 3, accuracy: 0.9417
epoch: 4, accuracy: 0.9465
epoch: 5, accuracy: 0.9531
epoch: 6, accuracy: 0.9513
epoch: 7, accuracy: 0.9554
epoch: 8, accuracy: 0.9568
epoch: 9, accuracy: 0.9595
# save
model.save("./result/ckpt_{}".format(epoch))
# restore
model = create_model()
model.restore("./result/ckpt_9")model = create_model()
model.restore("./result/ckpt_9")
y_pred = model(x)
y_pred = qb.softmax(y_pred)
print(y_pred)