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emotion_classifier_tensorflow_version/CNN/cnn.py

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+import tensorflow as tf
+from tensorflow.contrib.layers.python.layers import initializers
+
+
+class CNN_Model():
+ def __init__(self, num_tags_=7, lr_=0.001, channel_=1, hidden_dim_=1024, full_shape_=2304, optimizer_='Adam'):
+ self.num_tags = num_tags_
+ self.lr = lr_
+ self.full_shape = full_shape_
+ self.channel = channel_
+ self.hidden_dim = hidden_dim_
+ self.conv_feature = [32, 32, 32, 64]
+ self.conv_size = [1, 5, 3, 5]
+ self.maxpool_size = [0, 3, 3, 3]
+ self.maxpool_stride = [0, 2, 2, 2]
+ # self.initializer = tf.truncated_normal_initializer(stddev=0.05)
+ self.initializer = initializers.xavier_initializer()
+ self.dropout = tf.placeholder(dtype=tf.float32, name='dropout')
+ self.x_input = tf.placeholder(dtype=tf.float32, shape=[None, None, None, self.channel], name='x_input')
+ self.y_target = tf.placeholder(dtype=tf.int32, shape=[None], name='y_target')
+ self.batch_size = tf.shape(self.x_input)[0]
+ self.logits = self.project_layer(self.cnn_layer())
+ with tf.variable_scope("loss"):
+ self.loss = self.loss_layer(self.logits)
+ self.train_step = self.optimizer(self.loss, optimizer_)
+
+ def cnn_layer(self):
+ with tf.variable_scope("conv1"):
+ conv1_weight = tf.get_variable('conv1_weight', [self.conv_size[0], self.conv_size[0],
+ self.channel, self.conv_feature[0]],
+ dtype=tf.float32, initializer=self.initializer)
+ conv1_bias = tf.get_variable('conv1_bias', [self.conv_feature[0]], dtype=tf.float32,
+ initializer=tf.constant_initializer(0.0))
+ conv1 = tf.nn.conv2d(self.x_input, conv1_weight, [1, 1, 1, 1], padding='SAME')
+ conv1_add_bias = tf.nn.bias_add(conv1, conv1_bias)
+ conv1_relu = tf.nn.relu(conv1_add_bias)
+ norm1 = tf.nn.lrn(conv1_relu, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm1')
+ with tf.variable_scope("conv2"):
+ conv2_weight = tf.get_variable('conv2_weight', [self.conv_size[1], self.conv_size[1],
+ self.conv_feature[0], self.conv_feature[1]],
+ dtype=tf.float32, initializer=self.initializer)
+ conv2_bias = tf.get_variable('conv2_bias', [self.conv_feature[1]], dtype=tf.float32,
+ initializer=tf.constant_initializer(0.0))
+ conv2 = tf.nn.conv2d(norm1, conv2_weight, [1, 1, 1, 1], padding='SAME')
+ conv2_add_bias = tf.nn.bias_add(conv2, conv2_bias)
+ conv2_relu = tf.nn.relu(conv2_add_bias)
+ pool2 = tf.nn.max_pool(conv2_relu, ksize=[1, self.maxpool_size[1], self.maxpool_size[1], 1],
+ strides=[1, self.maxpool_stride[1], self.maxpool_stride[1], 1],
+ padding='SAME', name='pool_layer2')
+ norm2 = tf.nn.lrn(pool2, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm2')
+ with tf.variable_scope("conv3"):
+ conv3_weight = tf.get_variable('conv3_weight', [self.conv_size[2], self.conv_size[2],
+ self.conv_feature[1], self.conv_feature[2]],
+ dtype=tf.float32, initializer=self.initializer)
+ conv3_bias = tf.get_variable('conv3_bias', [self.conv_feature[2]], dtype=tf.float32,
+ initializer=tf.constant_initializer(0.0))
+ conv3 = tf.nn.conv2d(norm2, conv3_weight, [1, 1, 1, 1], padding='SAME')
+ conv3_add_bias = tf.nn.bias_add(conv3, conv3_bias)
+ conv3_relu = tf.nn.relu(conv3_add_bias)
+ pool3 = tf.nn.max_pool(conv3_relu, ksize=[1, self.maxpool_size[2], self.maxpool_size[2], 1],
+ strides=[1, self.maxpool_stride[2], self.maxpool_stride[2], 1],
+ padding='SAME', name='pool_layer3')
+ norm3 = tf.nn.lrn(pool3, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm3')
+ with tf.variable_scope("conv4"):
+ conv4_weight = tf.get_variable('conv4_weight', [self.conv_size[3], self.conv_size[3],
+ self.conv_feature[2], self.conv_feature[3]],
+ dtype=tf.float32, initializer=self.initializer)
+ conv4_bias = tf.get_variable('conv4_bias', [self.conv_feature[3]], dtype=tf.float32,
+ initializer=tf.constant_initializer(0.0))
+ conv4 = tf.nn.conv2d(norm3, conv4_weight, [1, 1, 1, 1], padding='SAME')
+ conv4_add_bias = tf.nn.bias_add(conv4, conv4_bias)
+ conv4_relu = tf.nn.relu(conv4_add_bias)
+ pool4 = tf.nn.max_pool(conv4_relu, ksize=[1, self.maxpool_size[3], self.maxpool_size[3], 1],
+ strides=[1, self.maxpool_stride[3], self.maxpool_stride[3], 1],
+ padding='SAME', name='pool_layer4')
+ norm4 = tf.nn.lrn(pool4, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm4')
+ return norm4
+
+ def cnn_layer_single(self):
+ with tf.variable_scope("conv1"):
+ conv1_weight = tf.get_variable('conv1_weight', [self.conv_size[0], self.conv_size[0],
+ self.channel, self.conv_feature[0]],
+ dtype=tf.float32, initializer=self.initializer)
+ conv1_bias = tf.get_variable('conv1_bias', [self.conv_feature[0]], dtype=tf.float32,
+ initializer=tf.constant_initializer(0.0))
+ conv1 = tf.nn.conv2d(self.x_input, conv1_weight, [1, 1, 1, 1], padding='SAME')
+ conv1_add_bias = tf.nn.bias_add(conv1, conv1_bias)
+ conv1_relu = tf.nn.relu(conv1_add_bias)
+ with tf.variable_scope("conv2"):
+ conv2_weight = tf.get_variable('conv2_weight', [self.conv_size[1], self.conv_size[1],
+ self.conv_feature[0], self.conv_feature[1]],
+ dtype=tf.float32, initializer=self.initializer)
+ conv2_bias = tf.get_variable('conv2_bias', [self.conv_feature[1]], dtype=tf.float32,
+ initializer=tf.constant_initializer(0.0))
+ conv2 = tf.nn.conv2d(conv1_relu, conv2_weight, [1, 1, 1, 1], padding='SAME')
+ conv2_add_bias = tf.nn.bias_add(conv2, conv2_bias)
+ conv2_relu = tf.nn.relu(conv2_add_bias)
+ pool2 = tf.nn.max_pool(conv2_relu, ksize=[1, self.maxpool_size[1], self.maxpool_size[1], 1],
+ strides=[1, self.maxpool_stride[1], self.maxpool_stride[1], 1],
+ padding='SAME', name='pool_layer2')
+ with tf.variable_scope("conv3"):
+ conv3_weight = tf.get_variable('conv3_weight', [self.conv_size[2], self.conv_size[2],
+ self.conv_feature[1], self.conv_feature[2]],
+ dtype=tf.float32, initializer=self.initializer)
+ conv3_bias = tf.get_variable('conv3_bias', [self.conv_feature[2]], dtype=tf.float32,
+ initializer=tf.constant_initializer(0.0))
+ conv3 = tf.nn.conv2d(pool2, conv3_weight, [1, 1, 1, 1], padding='SAME')
+ conv3_add_bias = tf.nn.bias_add(conv3, conv3_bias)
+ conv3_relu = tf.nn.relu(conv3_add_bias)
+ pool3 = tf.nn.max_pool(conv3_relu, ksize=[1, self.maxpool_size[2], self.maxpool_size[2], 1],
+ strides=[1, self.maxpool_stride[2], self.maxpool_stride[2], 1],
+ padding='SAME', name='pool_layer3')
+ with tf.variable_scope("conv4"):
+ conv4_weight = tf.get_variable('conv4_weight', [self.conv_size[3], self.conv_size[3],
+ self.conv_feature[2], self.conv_feature[3]],
+ dtype=tf.float32, initializer=self.initializer)
+ conv4_bias = tf.get_variable('conv4_bias', [self.conv_feature[3]], dtype=tf.float32,
+ initializer=tf.constant_initializer(0.0))
+ conv4 = tf.nn.conv2d(pool3, conv4_weight, [1, 1, 1, 1], padding='SAME')
+ conv4_add_bias = tf.nn.bias_add(conv4, conv4_bias)
+ conv4_relu = tf.nn.relu(conv4_add_bias)
+ pool4 = tf.nn.max_pool(conv4_relu, ksize=[1, self.maxpool_size[3], self.maxpool_size[3], 1],
+ strides=[1, self.maxpool_stride[3], self.maxpool_stride[3], 1],
+ padding='SAME', name='pool_layer4')
+ return pool4
+
+ def project_layer(self, x_in_):
+ with tf.variable_scope("project"):
+ with tf.variable_scope("hidden"):
+ x_in_ = tf.reshape(x_in_, [self.batch_size, -1])
+ w_tanh1 = tf.get_variable("w_tanh1", [self.full_shape, self.hidden_dim*2], initializer=self.initializer,
+ regularizer=tf.contrib.layers.l2_regularizer(0.001))
+ b_tanh1 = tf.get_variable("b_tanh1", [self.hidden_dim*2], initializer=tf.zeros_initializer())
+ w_tanh2 = tf.get_variable("w_tanh2", [self.hidden_dim*2, self.hidden_dim], initializer=self.initializer,
+ regularizer=tf.contrib.layers.l2_regularizer(0.001))
+ b_tanh2 = tf.get_variable("b_tanh2", [self.hidden_dim], initializer=tf.zeros_initializer())
+ output1 = tf.nn.dropout(tf.nn.relu(tf.add(tf.matmul(x_in_, w_tanh1),
+ b_tanh1)), keep_prob=self.dropout)
+ output2 = tf.nn.dropout(tf.nn.relu(tf.add(tf.matmul(output1, w_tanh2),
+ b_tanh2)), keep_prob=self.dropout)
+ with tf.variable_scope("output"):
+ w_out = tf.get_variable("w_out", [self.hidden_dim, self.num_tags], initializer=self.initializer,
+ regularizer=tf.contrib.layers.l2_regularizer(0.001))
+ b_out = tf.get_variable("b_out", [self.num_tags], initializer=tf.zeros_initializer())
+ pred_ = tf.add(tf.matmul(output2, w_out), b_out, name='logits')
+ return pred_
+
+ def loss_layer(self, project_logits):
+ with tf.variable_scope("loss"):
+ loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
+ logits=project_logits, labels=self.y_target), name='softmax_loss')
+ return loss
+
+ def optimizer(self, loss_, method=''):
+ if method == 'Momentum':
+ step = tf.Variable(0, trainable=False)
+ model_learning_rate = tf.train.exponential_decay(0.01, step,
+ 100, 0.99, staircase=True)
+ my_optimizer = tf.train.MomentumOptimizer(model_learning_rate, momentum=0.9)
+ train_step_ = my_optimizer.minimize(loss_, global_step=step, name='train_step')
+ print('Using ', method)
+ elif method == 'SGD':
+ step = tf.Variable(0, trainable=False)
+ model_learning_rate = tf.train.exponential_decay(0.1, step,
+ 200., 0.96, staircase=True)
+ my_optimizer = tf.train.GradientDescentOptimizer(model_learning_rate)
+ train_step_ = my_optimizer.minimize(loss_, name='train_step')
+ print('Using ', method)
+ elif method == 'Adam':
+ train_step_ = tf.train.AdamOptimizer(self.lr).minimize(loss_, name='train_step')
+ print('Using ', method)
+ else:
+ train_step_ = tf.train.MomentumOptimizer(0.005, momentum=0.9).minimize(loss_, name='train_step')
+ print('Using Default')
+ return train_step_