Firsh Push at 20241207

2024-12-07 02:00:39 +08:00
commit 4bd0ae023d
122 changed files with 2804 additions and 0 deletions
--- a/experiments/topic_model.py
+++ b/experiments/topic_model.py
@@ -0,0 +1,298 @@
+from convolution_model_tools.convolution_2D_tools import model_2D_tool
+from dense_model_tools.dense_tools import model_Dense_Layer
+from all_models_tools.all_model_tools import add_optimizers_function, add_Activative, add_dropout, call_back
+from keras.activations import softmax, sigmoid
+from keras.applications import VGG19, ResNet50, NASNetLarge, DenseNet201, Xception
+from keras.applications.efficientnet_v2 import EfficientNetV2L
+from keras.layers import BatchNormalization, Flatten, GlobalAveragePooling2D, MaxPooling2D, Dense, Conv2D, Dropout, TimeDistributed, LSTM, Input
+from keras import regularizers
+
+def one_layer_cnn_model():
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    img_Input = tools.add_2D_input()
+    x = tools.add_Convolution2D(img_Input, 32)
+    x = add_Activative(x)
+    x = tools.add_MaxPooling(x)
+
+    x = tools.add_Convolution2D(x, 64)
+    x = add_Activative(x)
+    x = tools.add_MaxPooling(x)
+
+    flatter = tools.add_flatten(x)
+
+    dense = dense_tool.add_dense(64, flatter)
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(32, dense)
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+    return img_Input, dense
+
+def find_example_cnn_model():
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    img_Input = tools.add_2D_input()
+
+    x = tools.add_Convolution2D(img_Input, 16)
+    x = add_Activative(x)
+    x = add_dropout(x, 0.25)
+
+    x = tools.add_Convolution2D(x, 32)
+    x = add_Activative(x)
+    x = add_dropout(x, 0.25)
+
+    x = tools.add_MaxPooling(x)
+
+    x = tools.add_Convolution2D(x, 64)
+    x = add_Activative(x)
+    x = add_dropout(x, 0.25)
+
+    x = tools.add_MaxPooling(x)
+
+    x = tools.add_Convolution2D(x, 128)
+    x = add_Activative(x)
+    x = add_dropout(x, 0.25)
+
+    x = tools.add_MaxPooling(x)
+
+    flatter = tools.add_flatten(x)
+
+    dense = dense_tool.add_dense(64, flatter)
+    dense = add_Activative(dense)
+    dense = add_dropout(dense, 0.25)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, sigmoid)
+
+    return img_Input, dense
+
+def change_example_cnn_model():
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    img_Input = tools.add_2D_input()
+
+    x = tools.add_Convolution2D(img_Input, 16)
+    x = add_Activative(x)
+    x = tools.add_batchnomlization(x)
+
+    x = tools.add_Convolution2D(x, 32)
+    x = add_Activative(x)
+    x = tools.add_batchnomlization(x)
+
+    x = tools.add_MaxPooling(x)
+
+    x = tools.add_Convolution2D(x, 64)
+    x = add_Activative(x)
+    x = tools.add_batchnomlization(x)
+
+    x = tools.add_MaxPooling(x)
+
+    x = tools.add_Convolution2D(x, 128)
+    x = add_Activative(x)
+    x = tools.add_batchnomlization(x)
+
+    x = tools.add_MaxPooling(x)
+
+    flatter = tools.add_flatten(x)
+
+    dense = dense_tool.add_dense(64, flatter)
+    dense = add_Activative(dense)
+    dense = add_dropout(dense, 0.3)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return img_Input, dense
+
+def two_convolution_cnn_model():
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    img_Input = tools.add_2D_input()
+    x = tools.add_two_floors_convolution2D(img_Input, 32)
+    x = tools.add_MaxPooling(x)
+
+    x = tools.add_two_floors_convolution2D(x, 64)
+    x = tools.add_MaxPooling(x)
+
+    flatter = tools.add_flatten(x)
+
+    dense = dense_tool.add_dense(64, flatter)
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(32, dense)
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+    return img_Input, dense  
+
+def VGG19_model():
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    vgg19 = VGG19(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(vgg19.output)
+    dense = dense_tool.add_dense(64, flatten)
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return vgg19, dense  
+
+def Resnet50_model():
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    vgg19 = ResNet50(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(vgg19.output)
+    dense = dense_tool.add_dense(64, flatten)
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return vgg19, dense  
+
+def DenseNet201_model():
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    Densenet201 = DenseNet201(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(Densenet201.output)
+    dense = dense_tool.add_dense(64, flatten)
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return Densenet201, dense
+
+def Xception_model():
+    xception = Xception(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = Flatten()(xception.output)
+    dense = Dense(units = 64, activation = "relu")(flatten)
+    dense = Dense(units = 7, activation = "softmax")(dense)
+
+    return xception, dense
+
+def cnn_LSTM():
+    head = Input(shape = (150, 150, 3))
+    inputs = Conv2D(filters = 64, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(head)
+    inputs = Conv2D(filters = 64, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = MaxPooling2D(strides = 2, pool_size = (2, 2))(inputs)
+    inputs = Dropout(0.25)(inputs)
+
+    inputs = Conv2D(filters = 128, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = Conv2D(filters = 128, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = MaxPooling2D(strides = 2, pool_size = (2, 2))(inputs)
+    inputs = Dropout(0.25)(inputs)
+
+    inputs = Conv2D(filters = 256, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = Conv2D(filters = 256, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = MaxPooling2D(strides = 2, pool_size = (2, 2))(inputs)
+    inputs = Dropout(0.25)(inputs)
+
+    inputs = Conv2D(filters = 512, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = Conv2D(filters = 512, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = Conv2D(filters = 512, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = MaxPooling2D(strides = 2, pool_size = (2, 2))(inputs)
+    inputs = Dropout(0.25)(inputs)
+
+    inputs = Conv2D(filters = 512, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = Conv2D(filters = 512, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = Conv2D(filters = 512, strides = 1, kernel_size = (3, 3), padding = "same", activation = "relu")(inputs)
+    inputs = MaxPooling2D(strides = 2, pool_size = (2, 2))(inputs)
+    inputs = Dropout(0.25)(inputs)
+    inputs = TimeDistributed(Flatten())(inputs)
+
+    inputs = LSTM(units = 49)(inputs)
+    inputs = Dense(units = 64)(inputs)
+    output = Dense(units = 7, activation = "softmax")(inputs)
+
+    return head, output
+
+def add_regularizers_L1(): # 比較正規化
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    Resnet50 = ResNet50(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(Resnet50.output)
+    dense = dense_tool.add_regularizer_dense(64, flatten, regularizers.L1())
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return Resnet50, dense
+
+def add_regularizers_L2(): # 比較正規化
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    Resnet50 = ResNet50(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(Resnet50.output)
+    dense = dense_tool.add_regularizer_dense(64, flatten, regularizers.L2())
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return Resnet50, dense
+
+def add_regularizers_L1L2(): # 比較正規化
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    Resnet50 = ResNet50(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(Resnet50.output)
+    dense = dense_tool.add_regularizer_dense(64, flatten, regularizers.L1L2())
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return Resnet50, dense
+
+def add_layers1_L2(Dense_layers): # 比較正規化
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+    layers = 32
+
+    Densenet201 = DenseNet201(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(Densenet201.output)
+
+    for layer in range(Dense_layers):
+        dense = dense_tool.add_regularizer_kernel_dense(unit = layers, input_data = flatten, regularizer = regularizers.L2())
+        dense = add_Activative(dense)
+        layers *= 2
+
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return Densenet201, dense
+
+def add_layers_another_L2(Dense_layers, layers): # 比較正規化
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    Densenet201 = DenseNet201(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(Densenet201.output)
+
+    for layer in range(Dense_layers):
+        dense = dense_tool.add_regularizer_dense(unit = layers, input_data = flatten, regularizer = regularizers.L2())
+        dense = add_Activative(dense)
+        layers /= 2
+
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return Densenet201, dense
+
+def add_bias_regularizers(): # 比較正規化
+    tools = model_2D_tool()
+    dense_tool = model_Dense_Layer()
+
+    Resnet50 = ResNet50(include_top = False, weights = "imagenet", input_shape = (120, 120, 3))
+    flatten = tools.add_flatten(Resnet50.output)
+    dense = dense_tool.add_regularizer_bias_dense(64, flatten, regularizers.L2())
+    dense = add_Activative(dense)
+    dense = dense_tool.add_dense(7, dense)
+    dense = add_Activative(dense, softmax)
+
+    return Resnet50, dense