In the Machine Learning/Data Science/Deep Learning End to End Project in Python Tutorial in Hindi, we explained each and every step of Machine Learning Project / Data Science Project / Deep Learning Project in detail.
Project name: Fashion MNIST Classification
What we cover in this Project:
- Import Libraries
- Load Data
- Show Image from Numbers
- Feature Scaling
- Build First Neural Network
- Train Model
- Test & Evaluate Model
- Confusion Matrix
- Classification Report
- Save Mode
Project Source Code
# -*- coding: utf-8 -*-
"""first deep learning project - MNIST-Fashion Classification.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1bCl8PUVo2XP8_-emIE3Zzi8jQ3Jsy2rH
# First Deep Learning Project
##Fashion Classification
### Train Neural Network on 60,000 Fashion-MNIST Images (data in NP array)
### Test Neural Network on 10,000 Fashion-MNIST Images (data in NP array)
"""
'''
class_labels:
0 => T-shirt/top
1 => Trouser
2 => Pullover
3 => Dress
4 => Coat
5 => Sandal
6 => Shirt
7 => Sneaker
8 => Bag
9 => Ankle boot
Classify the given input from above class using Neural Network
image shape 28 X 28 pixel ( Gray scale)
'''
"""### Import Libraries"""
import keras
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
"""## Load Data"""
(X_train, y_train), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()
X_train.shape, y_train.shape
X_test.shape, y_test.shape
X_train[0]
y_train[0]
class_labels = ["T-shirt/top","Trouser","Pullover","Dress","Coat","Sandal","Shirt","Sneaker","Bag","Ankle boot"]
'''
0 => T-shirt/top
1 => Trouser
2 => Pullover
3 => Dress
4 => Coat
5 => Sandal
6 => Shirt
7 => Sneaker
8 => Bag
9 => Ankle boot '''
plt.imshow(X_train[0], cmap ="Greys")
plt.figure(figsize=(16,16))
for i in range(25):
plt.subplot(5,5,i+1)
plt.imshow(X_train[i],cmap="Greys")
plt.axis('off')
plt.title(class_labels[y_train[i]]+"="+str(y_train[i]), fontsize=20)
'''
0 => T-shirt/top
1 => Trouser
2 => Pullover
3 => Dress
4 => Coat
5 => Sandal
6 => Shirt
7 => Sneaker
8 => Bag
9 => Ankle boot '''
"""# Feature Scalling"""
X_train = X_train/255
X_test = X_test/255
X_train[0]
"""## Build Neural Network"""
model = keras.models.Sequential([
keras.layers.Flatten(input_shape=[28,28]),
keras.layers.Dense(units=32, activation='relu'),
keras.layers.Dense(units=10, activation='softmax')
])
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=1)
model.fit(X_train, y_train, epochs=10)
"""## Test and Evaluate Neural Network Model"""
model.evaluate(X_test,y_test)
y_pred = model.predict(X_test)
y_pred[0].round(2)
np.argmax(y_pred[0].round(2))
'''
0 => T-shirt/top
1 => Trouser
2 => Pullover
3 => Dress
4 => Coat
5 => Sandal
6 => Shirt
7 => Sneaker
8 => Bag
9 => Ankle boot '''
y_test[0]
plt.figure(figsize=(16,16))
for i in range(25):
plt.subplot(5,5,i+1)
plt.imshow(X_test[i],cmap="Greys")
plt.axis('off')
plt.title("Actual= {} \n Predicted = {}".format(class_labels[y_test[i]], class_labels[np.argmax(y_pred[i])]))
"""## Confusion Matrix"""
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, [ np.argmax(i) for i in y_pred])
plt.figure(figsize=(16,9))
sns.heatmap(cm, annot=True, fmt = "d")
"""## Classification Report"""
from sklearn.metrics import classification_report
cr = classification_report(y_test, [ np.argmax(i) for i in y_pred], target_names = class_labels,)
print(cr)
"""## Save Model"""
model.save("MNIST_classifier_nn_model.h5")
model = keras.models.load_model("MNIST_classifier_nn_model.h5")
model.predict(X_test)
Hi sir please Make a series on a deep learning
Sure
Plz sir kears and tensorflow Uploaded videos