Fwd: Titanic-spaceship

[Hashsham Abid]

Hi All,

I have the following code, however not having a good accuracy and loss is nan

Could someone help me with this as I am new on machine learning.

# -*- coding: utf-8 -*-

"""Space_Ship_Titanic.ipynb

Automatically generated by Colaboratory.

Original file is located at

    https://colab.research.google.com/drive/1DM8gsVWCHQ4K1qAq22Si8DrVtTlikGrB

""

import os

os.environ['KAGGLE_CONFIG_DIR'] = '/content'

import pandas as pd

from tensorflow import keras

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler, MinMaxScaler

import numpy as np

# !kaggle competitions download -c spaceship-titanic

# !unzip \*.zip && rm *.zip

df = pd.read_csv("train.csv")

df1 = pd.read_csv("test.csv")

df.head()

df[['Deck', 'Num', 'Side']] = df['Cabin'].str.split("/",expand=True)

df1[['Deck', 'Num', 'Side']] = df1['Cabin'].str.split("/",expand=True)

df.head()

df = df.drop("Cabin",axis=1)

df1 = df1.drop("Cabin",axis=1)

df.head()

df["Billed"] = df[['RoomService','FoodCourt','ShoppingMall','Spa','VRDeck']].sum(axis=1)

df = df.drop(['RoomService','FoodCourt','ShoppingMall','Spa','VRDeck',],axis=1)

df1["Billed"] = df1[['RoomService','FoodCourt','ShoppingMall','Spa','VRDeck']].sum(axis=1)

df1 = df1.drop(['RoomService','FoodCourt','ShoppingMall','Spa','VRDeck',],axis=1)

df.head()

df = df.drop('Name',axis=1)

df1 = df1.drop('Name',axis=1)

df.head()

df[['Pass_group','group_num']] = df['PassengerId'].str.split("_",expand=True)

df = df.drop("PassengerId",axis=1)

df1[['Pass_group','group_num']] = df1['PassengerId'].str.split("_",expand=True)

df1 = df1.drop("PassengerId",axis=1)

df.head()

vocab_homePlanet = df["HomePlanet"].value_counts()

vocab_homePlanet

df['HomePlanet'] = df['HomePlanet'].dropna()

df['HomePlanet'].value_counts()

df["Destination"].value_counts()

df["Deck"].value_counts()

df.head()

df = df.astype(str)

df1 = df1.astype(str)

df

vectorize_layer = keras.layers.TextVectorization(max_tokens=50000,output_mode='int',output_sequence_length=4)

vectorize_layer.adapt(df['HomePlanet'])

df['HomePlanet'] = vectorize_layer(df['HomePlanet'])

vectorize_layer = keras.layers.TextVectorization(max_tokens=50000,output_mode='int',output_sequence_length=4)

vectorize_layer.adapt(df1['HomePlanet'])

df1['HomePlanet'] = vectorize_layer(df1['HomePlanet'])

df.head()

def vectorized_layer(data,input,output_length) :

  vectorize_layer = keras.layers.TextVectorization(max_tokens=5000,output_mode='int',output_sequence_length=output_length)

  vectorize_layer.adapt(data[input])

  return vectorize_layer(data[input])

df['Destination'].value_counts()

df['Destination'] = vectorized_layer(df,'Destination',4)

df1['Destination'].value_counts()

df1['Destination'] = vectorized_layer(df1,'Destination',4)

df['Deck'].value_counts()

df['Deck'] = vectorized_layer(df,'Deck',9)

df1['Deck'].value_counts()

df1['Deck'] = vectorized_layer(df1,'Deck',9)

df['Side'].value_counts()

df['Side'] = vectorized_layer(df,'Side',3)

df1['Side'].value_counts()

df1['Side'] = vectorized_layer(df1,'Side',3)

df.head()

df1.head()

df['CryoSleep'] = df['CryoSleep'].map({'True':1,'False':0})

df['VIP'] = df['VIP'].map({'True':1,'False':0})

df['Transported'] = df['Transported'].map({'True':1,'False':0})

df1['CryoSleep'] = df1['CryoSleep'].map({'True':1,'False':0})

df1['VIP'] = df1['VIP'].map({'True':1,'False':0})

# df1['Transported'] = df1['Transported'].map({'True':1,'False':0})

df.head()

df1.head()

x = df.drop(['Transported'],axis=1)

y = df['Transported']

# x_normalized = df[['CryoSleep','VIP']]

x = x.to_numpy(dtype='float32')

y = y.to_numpy(dtype='float32')

# x_test = df1.drop(['CryoSleep','VIP'],axis=1)

x_test = df1

# x_test_normalized = df[['CryoSleep','VIP']]

x_test = x_test.to_numpy(dtype='float32')

x.dtype, x_test.dtype

minMaxScaller = MinMaxScaler(feature_range=(0,1))

StdScaller = StandardScaler()

x_minMax = minMaxScaller.fit_transform(x)

x_minMax

x_test_minMax = minMaxScaller.fit_transform(x_test)

# x_Std = StdScaller.fit_transform(x)

# x_Std[1]

x_train,x_val,y_train,y_val = train_test_split(x_minMax,y,test_size=0.2,random_state=42)

x_train.shape, x_val.shape, y_train.shape, y_val.shape

model = keras.Sequential()

input_shape = x.shape[1]

input_shape

x_train.view()

# model.add(keras.layers.Embedding(500,16))

model.add(keras.layers.Dense(512,activation='relu',input_shape=(,input_shape)))

model.add(keras.layers.BatchNormalization())

model.add(keras.layers.Dropout(0.1))

model.add(keras.layers.Dense(256,activation='relu'))

model.add(keras.layers.BatchNormalization())

model.add(keras.layers.Dropout(0.1))

model.add(keras.layers.Dense(128,activation='relu'))

model.add(keras.layers.BatchNormalization())

model.add(keras.layers.Dropout(0.1))

# model.add(keras.layers.Flatten())

model.add(keras.layers.Dense(1,activation='sigmoid'))

optimizer = keras.optimizers.Adam(learning_rate=0.001)

model.compile(optimizer=optimizer,loss='mse',metrics=['accuracy'])

model.fit(x_train,y_train,epochs=20)

model.evaluate(x_test_minMax)

And this is the result that I am having

Epoch 1/20

218/218 [==============================] - 4s 8ms/step - loss: nan - accuracy: 0.4967

Epoch 2/20

218/218 [==============================] - 2s 7ms/step - loss: nan - accuracy: 0.4967

Epoch 3/20

218/218 [==============================] - 1s 6ms/step - loss: nan - accuracy: 0.4967

[Emirhan BULUT]

Hi,

I will help you for your notebook into the code with keras 2.0v in Python.

14 Mar 2024 Per 16:32 tarihinde Hashsham Abid <hashsha...@gmail.com> şunu yazdı:

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