23ws-LLMcoder/LLMcoder-GitHub-Python-Mix-Direct
Dataset Card for LLMcoder-GitHub-Python-Mix-Direct Python target autocomplete suggestions in the format of conversations for OpenAI's fine-tuning. Dataset Details Dataset Description Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed] Dataset Sources [optional] The data… See the full description on the dataset page: https://huggingface.co/datasets/23ws-LLMcoder/LLMcoder-GitHub-Python-Mix-Direct.
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1er('block3_conv3').output, block7_up])2 block7_conv1 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_merge)3 block7_conv2 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_conv1)4 block7_conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_conv2)5 6 block8_up = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(7 UpSampling2D(size=(2, 2))(block7_conv3))8 block8_merge = Concatenate(axis=3)([vgg16_model.get_layer('block2_conv2').output, block8_up])9 block8_conv1 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block8_merge)10 block8_conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block8_conv1)11 12 block9_up = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(13 UpSampling2D(size=(2, 2))(block8_conv2))14 block9_merge = Concatenate(axis=3)([vgg16_model.get_layer('block1_conv2').output, block9_up])15 block9_conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_merge)16 block9_conv2 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_conv1)17 18 block10_conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_conv2)19 block10_conv2 = Conv2D(1, 1, activation='sigmoid')(block10_conv1)20 21 model = Model(inputs=vgg16_model.input, outputs=block10_conv2)22 return model23 24 25if __name__ == '__main__':26 is_train = False27 if is_train:28 model = vgg10_unet(input_shape=(512,512,3), weights='imagenet')29 30 for index in range(15):31 model.layers[index].trainable = True32 model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])33 model_checkpoint = ModelCheckpoint('unet.h5', monitor='loss', verbose=1, save_best_only=True)34 model.fit_generator(train_generator(batch_size=4),35 steps_per_epoch=200,36 epochs=50,37 validation_data=train_generator(ba