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计算速度优化，摄像头读取逻辑改变，分类器更新。

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xinyang
2019-07-28 15:59:01 +08:00
parent a5259cbd1f
commit 5a0fdb30af
24 changed files with 17667 additions and 115529 deletions
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44
tools/TrainCNN/forward.py
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@@ -29,16 +29,22 @@ def max_pool_2x2(x):
CONV1_KERNAL_SIZE = 5
# 第一层卷积输出通道数
CONV1_OUTPUT_CHANNELS = 8
CONV1_OUTPUT_CHANNELS = 4
# 第二层卷积核大小
CONV2_KERNAL_SIZE = 3
# 第二层卷积输出通道数
CONV2_OUTPUT_CHANNELS = 16
CONV2_OUTPUT_CHANNELS = 6
# 第三层卷积核大小
CONV3_KERNAL_SIZE = 3
# 第三层卷积输出通道数
CONV3_OUTPUT_CHANNELS = 8
# 第一层全连接宽度
FC1_OUTPUT_NODES = 100
FC1_OUTPUT_NODES = 50
# 第二层全连接宽度（输出标签类型数）
FC2_OUTPUT_NODES = 15
@@ -49,6 +55,7 @@ OUTPUT_NODES = FC2_OUTPUT_NODES
def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
vars = []
vars_name = []
nodes = []
conv1_w = get_weight(
@@ -57,7 +64,10 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
conv1_b = get_bias([CONV1_OUTPUT_CHANNELS])
conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
pool1 = avg_pool_2x2(conv1)
print("conv1: ", conv1.shape)
print("pool1: ", pool1.shape)
vars.extend([conv1_w, conv1_b])
vars_name.extend(["conv1_w", "conv1_b"])
nodes.extend([conv1, pool1])
conv2_w = get_weight(
@@ -66,27 +76,41 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
conv2_b = get_bias([CONV2_OUTPUT_CHANNELS])
conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
pool2 = avg_pool_2x2(conv2)
print("conv2: ", conv2.shape)
print("pool2: ", pool2.shape)
vars.extend([conv2_w, conv2_b])
vars_name.extend(["conv2_w", "conv2_b"])
nodes.extend([conv2, pool2])
pool_shape = pool2.get_shape().as_list()
node = pool_shape[1] * pool_shape[2] * pool_shape[3]
reshaped = tf.reshape(pool2, [-1, node])
conv3_w = get_weight(
[CONV3_KERNAL_SIZE, CONV3_KERNAL_SIZE, CONV2_OUTPUT_CHANNELS, CONV3_OUTPUT_CHANNELS]
)
conv3_b = get_bias([CONV3_OUTPUT_CHANNELS])
conv3 = tf.nn.relu(tf.nn.bias_add(conv2d(pool2, conv3_w), conv3_b))
print("conv3: ", conv3.shape)
vars.extend([conv3_w, conv3_b])
vars_name.extend(["conv3_w", "conv3_b"])
nodes.extend([conv3])
conv_shape = conv3.get_shape().as_list()
node = conv_shape[1] * conv_shape[2] * conv_shape[3]
reshaped = tf.reshape(conv3, [-1, node])
reshaped = tf.nn.dropout(reshaped, keep_rate)
print("reshaped: ", reshaped.shape)
fc1_w = get_weight([node, FC1_OUTPUT_NODES], regularizer)
fc1_b = get_bias([FC1_OUTPUT_NODES])
fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
fc1 = tf.nn.dropout(fc1, keep_rate)
vars.extend([fc1_w, fc1_b])
vars_name.extend(["fc1_w", "fc1_b"])
nodes.extend([fc1])
fc2_w = get_weight([FC1_OUTPUT_NODES, FC2_OUTPUT_NODES], regularizer)
fc2_b = get_bias([FC2_OUTPUT_NODES])
# fc2 = tf.nn.softmax(tf.matmul(fc1, fc2_w) + fc2_b)
fc2 = tf.matmul(fc1, fc2_w) + fc2_b
fc2 = tf.matmul(fc1, fc2_w) + fc2_b
vars.extend([fc2_w, fc2_b])
vars_name.extend(["fc2_w", "fc2_b"])
nodes.extend([fc2])
return nodes, vars
return nodes, vars, vars_name