Tensorflow-keras实战（七）：tf.data.Dataset

淡淡的烟草味﹌ 2024-04-19 10:35 53阅读 0赞

# 目录： #

1.基础api  
2.tf\_data\_generate\_csv及与keras结合实战

### 1.基础api ###

### 2.tf\_data\_generate\_csv及与keras结合实战 ###

import matplotlib as mpl
    import matplotlib.pyplot as plt
    %matplotlib inline
    import numpy as np
    import pandas as pd
    import os
    import sklearn
    import sys
    import time
    import tensorflow as tf
    
    from tensorflow import keras
    
    print(tf.__version__)
    print(sys.version_info)
    for module in mpl,np,pd,sklearn,tf,keras:
        print(module.__name__, module.__version__)
    
    
    from sklearn.datasets import fetch_california_housing
    housing = fetch_california_housing()
    
    
    
    from sklearn.model_selection import train_test_split
    x_train_all, x_test, y_train_all, y_test = train_test_split(
        housing.data, housing.target, random_state = 7)
    x_train, x_valid, y_train, y_valid = train_test_split(
        x_train_all, y_train_all, random_state = 11)
    print(x_valid.shape, y_valid.shape)
    print(x_train.shape, y_train.shape)
    print(x_test.shape, y_test.shape)
    
    
    from sklearn.preprocessing import StandardScaler
    scaler = StandardScaler()
    x_train_scaled = scaler.fit_transform(x_train)
    x_valid_scaled = scaler.fit_transform(x_valid)
    x_test_scaled = scaler.fit_transform(x_test)
    
    
    
    
    output_dir = "generate_csv"
    if not os.path.exists(output_dir):
        os.mkdir(output_dir)
        
    def save_to_csv(output_dir,data,name_prefix,header=None,n_parts=10):
        path_format = os.path.join(output_dir,"{}_{:02d}.csv")
        filenames = []
        
        for file_idx,row_indices in enumerate(np.array_split(np.arange(len(data)),n_parts)):
            part_csv = path_format.format(name_prefix,file_idx)
            filenames.append(part_csv)
            with open(part_csv,"wt",encoding="utf-8") as f:
                if header is not None:
                    f.write(header+"\n")
                for row_index in row_indices:
                    f.write(",".join([repr(col) for col in data[row_index]]))
                    f.write('\n')
        return filenames
    
    train_data = np.c_[x_train_scaled,y_train]
    valid_data = np.c_[x_valid_scaled,y_valid]
    test_data = np.c_[x_test_scaled,y_test]
    header_cols = housing.feature_names + ["MidianHouseValue"]
    header_str = ",".join(header_cols)
    
    train_filenames = save_to_csv(output_dir,train_data,"train",header_str,n_parts=20)
    valid_filenames = save_to_csv(output_dir,valid_data,"valid",header_str,n_parts=10)
    test_filenames = save_to_csv(output_dir,test_data,"test",header_str,n_parts=10)
    
    
    
    
    import pprint
    print("train filenames:")
    pprint.pprint(train_filenames)
    print("valid filenames:")
    pprint.pprint(valid_filenames)
    print("test filename:")
    pprint.pprint(test_filenames)
    
    
    
    
    
    # 1.filename->dataset
    # 2.read file->dataset->datasets->merge
    # 3.parse csv
    
    filename_dataset = tf.data.Dataset.list_files(train_filenames)
    #list_files专门处理文件名，会把文件名生成Dataset
    for filename in filename_dataset:
        print(filename)
    
    n_readers = 5
    dataset = filename_dataset.interleave(
        lambda filename:tf.data.TextLineDataset(filename).skip(1),
        cycle_length = n_readers
    )
    for line in dataset.take(15):
        print(line.numpy())
    
    
    # tf.io.decode_csv(str, recode_dafaults)
    
    sample_str = '1,2,3,4,5'
    record_defaults = [
        tf.constant(0,dtype = tf.int32),
        0,
        np.nan,
        "hello",
        tf.constant([])
    ]
    parsed_fields = tf.io.decode_csv(sample_str,record_defaults)
    print(parsed_fields)
    
    
    def parse_csv_line(line,n_fields=9):
        defs = [tf.constant(np.nan)]*n_fields
        parsed_fields = tf.io.decode_csv(line,record_defaults=defs)
        x = tf.stack(parsed_fields[0:-1])
        y = tf.stack(parsed_fields[-1:])
        return x,y
    
    parse_csv_line(b'0.6363646332204844,-1.0895425985107923,0.09260902815633619,-0.20538124656801682,1.2025670451003232,-0.03630122549633783,-0.6784101660505877,0.182235342347858,2.429',
                  n_fields=9)
    
    
    # 1.filename->dataset
    # 2.read file->dataset->datasets->merge
    # 3.parse csv
    
    def csv_reader_dataset(filenames,n_readers=5,batch_size=32,n_parse_threads=5,
                          shuffle_buffer_size=10000):
        dataset = tf.data.Dataset.list_files(filenames)
        dataset = dataset.repeat()
        dataset = dataset.interleave(
            lambda filename:tf.data.TextLineDataset(filename).skip(1),
            cycle_length = n_readers)
        dataset.shuffle(shuffle_buffer_size)
        dataset = dataset.map(parse_csv_line,
                              num_parallel_calls = n_parse_threads)
        dataset = dataset.batch(batch_size)
        return dataset
    
    train_set = csv_reader_dataset(train_filenames,batch_size=3)
    for x_batch,y_batch in train_set.take(2):
        print("x:")
        pprint.pprint(x_batch)
        print("y:")
        pprint.pprint(y_batch)
    
    
    batch_size=32
    train_set = csv_reader_dataset(train_filenames,
                                  batch_size=batch_size)
    valid_set = csv_reader_dataset(valid_filenames,
                                 batch_size=batch_size)
    test_set = csv_reader_dataset(test_filenames,
                                 batch_size=batch_size)
    
    
    
    model = keras.models.Sequential([
        keras.layers.Dense(30,activation="relu",
                          input_shape=[8]),
        keras.layers.Dense(1),
    ])
    
    model.compile(loss="mean_squared_error",optimizer="sgd")
    callbacks = [keras.callbacks.EarlyStopping(
        patience=5,min_delta=1e-2)]
    
    history = model.fit(train_set,
                        validation_data = valid_set,
                        steps_per_epoch = 11160//batch_size,
                        validation_steps = 3870//batch_size,
                        epochs = 100,
                        callbacks = callbacks)
    
    
    
    
    model.evaluate(test_set,steps = 5160//batch_size)