import os os.environ['KAGGLE_USERNAME'] = 'spartasoommni' # username os.environ['KAGGLE_KEY'] = 'cf783e19878a7a9e8284d2ea28c7517c' # key
!kaggle datasets download -d oddrationale/mnist-in-csv
!unzip mnist-in-csv.zip
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.optimizers import Adam, SGD
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
train_df = pd.read_csv('mnist_train.csv')
test_df = pd.read_csv('mnist_test.csv')
plt.figure(figsize=(16, 10))
sns.countplot(train_df['label'])
plt.show()
train_df = train_df.astype(np.float32)
x_train = train_df.drop(columns=['label'], axis=1).values
y_train = train_df[['label']].values
test_df = test_df.astype(np.float32)
x_test = test_df.drop(columns=['label'], axis=1).values
y_test = test_df[['label']].values
index = 1
plt.title(str(y_train[index]))
plt.imshow(x_train[index].reshape((28, 28)), cmap='gray')
plt.show()
encoder = OneHotEncoder()
y_train = encoder.fit_transform(y_train).toarray()
y_test = encoder.fit_transform(y_test).toarray()
encoder = OneHotEncoder()
y_train = encoder.fit_transform(y_train).toarray()
y_test = encoder.fit_transform(y_test).toarray()
x_train = x_train / 255.
x_test = x_test / 255.
input = Input(shape=(784,))
hidden = Dense(1024, activation='relu')(input)
hidden = Dense(512, activation='relu')(hidden)
hidden = Dense(256, activation='relu')(hidden)
output = Dense(10, activation='softmax')(hidden)
model = Model(inputs=input, outputs=output)
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['acc'])
model.summary()
warnings.filterwarnings(action='ignore')
history = model.fit(
x_train,
y_train,
validation_data=(x_test, y_test),
epochs=20
)
