def test_full_net():
"""
test whether an evaluable network can be created
"""
#import
test_data = import_files.import_batch("/Users/admin/Documents/code/python/tensorflow/projects/CIFAR-10-convnet/Data/test/", 1, 10)
#weights and biases
layer1_weights = layer.init_weights(32, (4,4,3))
layer1_biases = layer.init_biases(32)
layer2_weights = layer.init_weights(128, (8192))
layer2_biases = layer.init_biases(128)
layer3_weights = layer.init_weights(10, (128))
layer3_biases = layer.init_biases(10)
output = np.empty((10, 10))
for i in range(0,9):
layer1 = layer.relu(layer.conv_layer(test_data[i], layer1_weights, layer1_biases, zero_pad_dimensions=(2,2), stride=(2,2)))
layer2 = layer.relu(layer.fulcon_layer(layer1, layer2_weights, layer2_biases))
layer3 = layer.relu(layer.fulcon_layer(layer2, layer3_weights, layer3_biases))
print(layer3)
output[i] = layer.softmax(layer3)
def decoder(self,input_z,name = 'generate_img',is_training = True):
hidden_num = 64
output_dim = 64
with tf.variable_scope(name,reuse = tf.AUTO_REUSE):
x = ly.fc(input_z, hidden_num * 8 * (output_dim // 16) * (output_dim // 16),name = 'gen_fc_0')
x = tf.reshape(x, shape=[self.imle_deep, output_dim // 16, output_dim // 16, hidden_num * 8]) ## 4, 4, 8*64
x = ly.deconv2d(x,hidden_num * 4,name = 'g_deconv2d_0') ### 8,8, 256
x = ly.batch_normal(x,name = 'g_deconv_bn_0',is_training = is_training)
x = ly.relu(x)
x = ly.deconv2d(x,hidden_num * 2,name = 'g_deconv2d_1') ### 16,16, 128
x = ly.batch_normal(x,name = 'g_deconv_bn_1',is_training = is_training)
x = ly.relu(x)
x = ly.deconv2d(x,hidden_num,name = 'g_deconv2d_2') ### 32,32, 64
x = ly.batch_normal(x,name = 'g_deconv_bn_2',is_training = is_training)
x = ly.relu(x)
x = ly.deconv2d(x, 3, name = 'g_deconv2d_3') ### 64,64, 3
x = ly.batch_normal(x,name = 'g_deconv_bn_3',is_training = is_training)
x = tf.nn.tanh(x)
return x
def discriminator(self,
x,
name='discriminator_img',
is_training=True): ## 64,64,3
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
x = ly.conv2d(x, 64, strides=2, use_bias=True,
name='d_conv_0') ## 32,32,64
x = ly.batch_normal(x, name='d_bn_0', is_training=is_training)
x = ly.relu(x, 0.2)
x = ly.conv2d(x, 128, strides=2, use_bias=True,
name='d_conv_1') ## 16,16,128
x = ly.batch_normal(x, name='d_bn_1', is_training=is_training)
x = ly.relu(x, 0.2)
x = ly.conv2d(x, 256, strides=2, use_bias=True,
name='d_conv_2') ## 8,8,256
x = ly.batch_normal(x, name='d_bn_2', is_training=is_training)
x = ly.relu(x, 0.2)
x = ly.conv2d(x, 512, strides=2, use_bias=True,
name='d_conv_3') ## 4,4,512
x = ly.batch_normal(x, name='d_bn_3', is_training=is_training)
x = ly.relu(x, 0.2)
x = ly.fc(x, 1, name='fc_0')
x = tf.nn.sigmoid(x)
return x
def old_tests():
print(backprop.final_layer_error(layer.softmax(np.array([5,2,5,1])), np.array([0,1,0,0]), np.array([5,2,5,1])))
print(layer.conv_layer(np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]]), layer.init_weights(5, (2,2,3)), layer.init_biases(5), zero_pad_dimensions=(2,2)))
print(layer.conv_layer(
layer.relu(
layer.conv_layer(np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]]), layer.init_weights(5, (2,2,3)), layer.init_biases(5), zero_pad_dimensions=(2,2))),
layer.init_weights(32, (2,2,5)),
layer.init_biases(32),
zero_pad_dimensions=(1,1)
).shape)
import_files.import_batch("/Users/admin/Documents/code/python/tensorflow/projects/CIFAR-10-convnet/Data/test/", 1, 256)
def __init__(self):
super(CNN_modified,self).__init__()
self.relu = layer.relu(2)
self.conv1 = layer.Conv(1,32,kernel_size=5,padding=2,stride=1)
self.pool1 = torch.nn.AvgPool2d(kernel_size=2,stride=2)
self.conv2 = layer.Conv(32,64,kernel_size=5,padding=2,stride=1)
self.pool2 = torch.nn.AvgPool2d(kernel_size=2,stride=2)
self.dense1 = layer.Dense(7*7*64,1024)
self.dense2 = layer.Dense(1024,10)
self.precision = 0.
self.epoch = 0
def run(model, train_set, vali_set, test_set):
for epoch in range(1, 300):
train_loss = train(model, train_set)
vali_loss = validation(model, vali_set)
accuracy = test(model, test_set)
print("epoch:", epoch, "\ttrain_loss:", train_loss, "\tvali_loss:", vali_loss, "\taccuracy:", accuracy)
lr = 0.01
model = net.model(optimizer.Adam(lr=lr)) # 30 66
#model = net.model(optimizer.GradientDescent(lr=lr)) #30번에 32퍼 학,검,테 데이터셋 128개일때
model.add(nn.conv2d(filters=32, kernel_size=[3,3], strides=[1,1], w_init=init.he))
model.add(nn.relu())
model.add(nn.maxpool2d(kernel_size=[2,2], strides=[2,2]))
model.add(nn.dropout(0.6))
model.add(nn.conv2d(filters=64, kernel_size=[3,3], strides=[1,1], w_init=init.he))
model.add(nn.relu())
model.add(nn.maxpool2d(kernel_size=[2,2], strides=[2,2]))
model.add(nn.dropout(0.6))
model.add(nn.conv2d(filters=128, kernel_size=[3,3], strides=[1,1], w_init=init.he))
model.add(nn.relu())
model.add(nn.maxpool2d(kernel_size=[2,2], strides=[2,2]))
model.add(nn.dropout(0.6))
model.add(nn.flatten())
model.add(nn.affine(out_dim=10, w_init=init.he))
def classify(self,
d_opt=None,
name='classify',
is_training=True): ### 64,64,1
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
x = tf.pad(self.input_img, [[0, 0], [5, 5], [5, 5], [0, 0]],
"REFLECT")
x = ly.conv2d(x,
64,
kernal_size=11,
name='conv_0',
padding='VALID',
use_bias=True)
x = ly.batch_normal(x, name='bn_0', is_training=is_training)
x = ly.relu(x)
x = ly.maxpooling2d(x) ## 32,32,64
x = tf.pad(x, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT")
x = ly.conv2d(x,
128,
kernal_size=7,
name='conv_1',
padding='VALID',
use_bias=True)
x = ly.batch_normal(x, name='bn_1', is_training=is_training)
x = ly.relu(x)
x = ly.maxpooling2d(x) ## 16,16,128
x = tf.pad(x, [[0, 0], [2, 2], [2, 2], [0, 0]], "REFLECT")
x = ly.conv2d(x,
256,
kernal_size=5,
name='conv_2',
padding='VALID',
use_bias=True)
x = ly.batch_normal(x, name='bn_2', is_training=is_training)
x = ly.relu(x)
x = ly.maxpooling2d(x) ## 8,8,256
x = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
x = ly.conv2d(x,
512,
kernal_size=3,
name='conv_3',
padding='VALID',
use_bias=True)
x = ly.batch_normal(x, name='bn_3', is_training=is_training)
x = ly.relu(x)
x = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
x = ly.conv2d(x,
512,
kernal_size=3,
name='conv_4',
padding='VALID',
use_bias=True)
x = ly.batch_normal(x, name='bn_4', is_training=is_training)
x = ly.relu(x)
x = ly.maxpooling2d(x) ## 4,4,512
x = ly.fc(x, 1024, name='fc_0', use_bias=True)
x = ly.batch_normal(x, name='bn_5', is_training=is_training)
x = ly.relu(x)
x = tf.nn.dropout(x, keep_prob=0.5)
x = ly.fc(x, self.class_num, name='fc_1', use_bias=True)
self.pred_x_index = tf.argmax(tf.nn.softmax(x), axis=-1)
self.pred_x_value = tf.reduce_max(tf.nn.softmax(x), axis=-1)
if (is_training):
cross_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.input_label, logits=x),
axis=0)
l2_loss = 0.0005 * tf.reduce_sum([
tf.nn.l2_loss(var)
for var in self.get_single_var('classify/fc')
])
loss = cross_loss + l2_loss
self.summaries.append(tf.summary.scalar('loss', loss))
_grad = d_opt.compute_gradients(
loss, var_list=self.get_vars('classify'))
train_op = d_opt.apply_gradients(_grad)
return train_op