def _inference(self, X, keep_prob): h = F.max_pool(F.activation(F.conv(X, 64))) h = F.max_pool(F.activation(F.conv(h, 128))) h = F.max_pool(F.activation(F.conv(h, 256))) h = F.activation(F.dense(F.flatten(h), 1024)) h = F.dense(h, self._num_classes) return tf.nn.softmax(h)
def _inference(self, X, keep_prob, is_train): dropout_rate = [0.9, 0.8, 0.7, 0.6, 0.5] layers = [64, 128, 256, 512, 512] iters = [2, 2, 3, 3] h = X # VGG Network Layer for i in range(4): for j in range(iters[i]): with tf.variable_scope('layers%s_%s' % (i, j)) as scope: h = F.conv(h, layers[i]) h = F.batch_norm(h, is_train) h = F.activation(h) h = F.dropout(h, dropout_rate[i], is_train) h = F.max_pool(h) # Fully Connected Layer with tf.variable_scope('fully_connected_layer') as scope: h = F.dense(h, layers[i + 1]) h = F.batch_norm(h, is_train) h = F.activation(h) h = F.dropout(h, dropout_rate[i + 1], is_train) # Softmax Layer with tf.variable_scope('softmax_layer') as scope: h = F.dense(h, self._num_classes) return h
def _inference(self, X, keep_prob): h = F.max_pool(F.activation(F.conv(X, 64))) h = F.max_pool(F.activation(F.conv(h, 128))) h = F.max_pool(F.activation(F.conv(h, 256))) h = F.activation(F.dense(F.flatten(h), 1024)) h = F.dense(h, self._num_classes) return h
def _inference(self, CC, MLO, keep_prob, is_train): layers = [3, 16, 32, 64, 64] cc = CC mlo = MLO for i in range(4): with tf.variable_scope('CC_layers_%s' % i) as scope: cc = F.conv(cc, layers[i]) cc = F.batch_norm(cc, is_train) cc = F.activation(cc) cc = F.max_pool(cc) with tf.variable_scope('CC_features') as scope: cc = F.dense(cc, layers[i + 1]) cc = F.batch_norm(cc, is_train) cc = F.activation(cc) for j in range(4): with tf.variable_scope('MLO_layers_%s' % j) as scope: mlo = F.conv(mlo, layers[j]) mlo = F.batch_norm(mlo, is_train) mlo = F.activation(mlo) mlo = F.max_pool(mlo) with tf.variable_scope('MLO_features') as scope: mlo = F.dense(mlo, layers[j + 1]) mlo = F.batch_norm(mlo, is_train) mlo = F.activation(mlo) with tf.variable_scope('softmax') as scope: concat = tf.concat(1, [cc, mlo]) h = F.dense(concat, self._num_classes) return h
def _inference(self, X, keep_prob): h = X h = F.activation(F.batch_normalization(F.conv(h, 16, bias_term=False))) for i in range(self._layers): h = self._residual(h, channels=16, strides=1) for channels in [32, 64]: for i in range(self._layers): strides = 2 if i == 0 else 1 h = self._residual(h, channels, strides) h = tf.reduce_mean(h, reduction_indices=[1, 2]) # Global Average Pooling h = F.dense(h, self._num_classes) return h
def _inference(self, X, keep_prob, is_train): h = F.conv(X, 16) for i in range(self._layers): with tf.variable_scope(str(16*self._k)+'_layers_%s' %i): h = self._residual(h, channels=16*self._k, strides=1, keep_prob=keep_prob, is_train=is_train) for channels in [32*self._k, 64*self._k]: for i in range(self._layers): with tf.variable_scope(str(channels)+'_layers_%s' %i): strides = 2 if i == 0 else 1 h = self._residual(h, channels, strides, keep_prob, is_train) h = F.activation(F.batch_norm(self, 'bn', h, is_train)) h = tf.reduce_mean(h, reduction_indices=[1,2]) h = F.dense(h, self._num_classes) return h
def _inference(self, X, keep_prob, is_train): # Conv_layer 1 conv = F.conv(X, 192) batch_norm = F._batch_norm(self, 'bn1', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.9, is_train) conv = F.conv(dropout, 192) batch_norm = F._batch_norm(self, 'bn2', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.9, is_train) max_pool = F.max_pool(dropout) # 16 x 16 # Conv_layer 2 conv = F.conv(max_pool, 192) batch_norm = F._batch_norm(self, 'bn3', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.8, is_train) conv = F.conv(dropout, 192) batch_norm = F._batch_norm(self, 'bn4', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.8, is_train) max_pool = F.max_pool(dropout) # 8 x 8 # Conv_layer 3 conv = F.conv(max_pool, 256) batch_norm = F._batch_norm(self, 'bn5', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.7, is_train) conv = F.conv(dropout, 256) batch_norm = F._batch_norm(self, 'bn6', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.7, is_train) conv = F.conv(dropout, 256) batch_norm = F._batch_norm(self, 'bn7', conv, is_train) dropout = F.dropout(relu, 0.7, is_train) max_pool = F.max_pool(dropout) # 4 x 4 # Conv_layer 4 conv = F.conv(max_pool, 512) batch_norm = F._batch_norm(self, 'bn8', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.6, is_train) conv = F.conv(dropout, 512) batch_norm = F._batch_norm(self, 'bn9', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.6, is_train) conv = F.conv(max_pool, 512) batch_norm = F._batch_norm(self, 'bn10', conv, is_train) relu = F.activation(batch_norm) dropout = F.dropout(relu, 0.6, is_train) max_pool = F.max_pool(dropout) # 2 x 2 # Fully Connected Layer h = tf.reduce_mean(max_pool, reduction_indices=[1,2]) h = F.dropout(h, 0.5, is_train) h = F.dense(h, 512) h = F._batch_norm(self, 'bn11', h, is_train) h = F.activation(h) h = F.dropout(h, 0.5, is_train) h = F.dense(h, self._num_classes) return h