AIKB-020 import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pathlib
import os
Let!
# change plt
def read_csv(filename?example_file.csv':
return pd.read_csv(filename)
def get_train(model_file.csv):
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_test(test_file.csv):
data = get_test(test_file.csv)
return data.reviewing().reindex
def get_train(model_file.csv):
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_test(test_file.csv):
data = get_test(test_file.csv)
return data.reviewing().reindex
def read_csv(filename?example_file.csv':
return pd.read_csv(filename)
def get_train(model_file.csv):
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_train(model_file.csv):
data = get_train(model_file.csv)
return data.reviewing().reindex
Get_df = read_csv(get_train(model_file.csv))
return get_df
# Load CSV with tensorflow
def reading_csv(example_file.csv'):
return pd.read_csv(example_file.csv)
plant_csv = read_csv(plant_data.csv')
# node image
netflix_img = np
1/2/510
Τ plant_csv = read_csv(plant_data.csv')
# node image
netflix_img = np
# def change plt
def read_csv(filename?example_file.csv':
return pd.read_csv(filename)
def get_train(model_file.csv):
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_test(test_file.csv):
data = get_test(test_file.csv)
return data.reviewing().reindex
def get_train(model_file.csv):
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_test(test_file.csv):
data = get_test(test_file.csv)
return data.reviewing().reindex
def read_csv(filename?example_file.csv':
return pd.read_csv(filename)
def get_train(model_file.csv):
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_train(model_file.csv:
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_train(model_file.csv:
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_train(model_file.csv:
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_train(model_file.csv:
data = get_train(model_file.csv)
return data.reviewing().reindex
def get_train(model_file.csv:
data = get_train(model_file.csv
quality.csv'
return pd.read_csv(quality.csv')
return pd.read_csv(quality.csv'
image_csv = read_csv(image_data.csv')
Compare imag = pd.read_csv(example_file.csv'):
change_csv = pd.read_csv(model_file.csv):
return pd.read_csv(model_file.csv):
he data = get_train(model_file.csv):
return data.reviewing().reindex
def get_test(test_file.csv):
data = get_test(test_file.csv):
return data.reviewing().reindex
def get_train(model_file.csv):
data = get_train(model_file.csv):
return data.reviewing().reindex
def get_test(test_file.csv):
data = get_test(test_file.csv):
return data.resterviewing().reindex
def get_train(model_file.csv
Pet Train = pd.read_csv('Train.csv')
1/3/510
return soil_data.reviewing().reindex
strain_data = pd.read_csv(plant_file.csv');
# thermal constants
def read csv(game_quality.csv'):
return pd.read_csv(game_quality.csv')
matrix_log = read_csv(method_file.csv', matrix_log= method.csv');
find_game = read_csv(image_file.csv'):
return pd.read_csv(image_file.csv'):
return pd.read_csv(image_file.csv'):
St data = get_train(model_file.csv):
return data.reviewing().reindex
def get_train(model_file.csv):
data = get_train(model_file.csv):
return data.reviewing().reindex
def get_test(t_test_file.csv):
data = get_test(test_file.csv):
return data.reviewing().reindex
def get_train(model_file.csv):
data = get_train(model_file.csv):
return data.reviewing().reindex
def get_train(model_file.csv:
data = get_train(model_file.csv:
return data.reviewing().reindex
def read_csv(filename?example_file.csv):
return pd.read_csv(filename)
''
'''train_data = pd.read_csv('Pet_train.csv')
create netflix_csv = read_csv('example_file.csv'):
return pd.read_csv(filename)
''
'''''csv = read_csv(example_file.csv'):
return pd.read_csv(example_file.csv')
'H.
TF.Tensor = tf.Tensor
import tensorflow as tf
import numpy as npqquality.csv cropped.png
0/7/510
2013年9月25日
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