Clothing Detection
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#Importing Packages
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt#Importing Keras Dataset
data = keras.datasets.fashion_mnist
#train and test data segregation
(train_images, train_labels), (test_images, test_labels) = data.load_data()
class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle Boot"]
train_images = train_images/255.0
test_images = test_images/255.0Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
32768/29515 [=================================] - 0s 3us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26427392/26421880 [==============================] - 25s 1us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz
8192/5148 [===============================================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz
4423680/4422102 [==============================] - 4s 1us/step
#Create a object of model class
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28,28)),
keras.layers.Dense(128, activation="relu"),
keras.layers.Dense(10, activation="softmax")
])
#Compile the model with MSE loss and Adam optimizer
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
#fitting the model
model.fit(train_images, train_labels, epochs=5)WARNING:tensorflow:From C:\Users\vinay\Anaconda3\lib\site-packages\tensorflow\python\ops\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
Epoch 1/5
60000/60000 [==============================] - 8s 132us/sample - loss: 0.4980 - acc: 0.8253
Epoch 2/5
60000/60000 [==============================] - 8s 127us/sample - loss: 0.3717 - acc: 0.8662
Epoch 3/5
60000/60000 [==============================] - 6s 93us/sample - loss: 0.3341 - acc: 0.8787
Epoch 4/5
60000/60000 [==============================] - 8s 130us/sample - loss: 0.3126 - acc: 0.8852
Epoch 5/5
60000/60000 [==============================] - 6s 92us/sample - loss: 0.2928 - acc: 0.8929
<tensorflow.python.keras.callbacks.History at 0x25f99983fd0>
#model evaluation
test_loss, test_acc = model.evaluate(test_images, test_labels)
print("Accuracy: ", test_acc)
prediction = model.predict(test_images)
#displaying predictions
for i in range(7):
plt.grid(False)
plt.imshow(test_images[i], cmap=plt.cm.binary)
plt.xlabel("Actual: " + class_names[test_labels[i]])
plt.title("Prediction " + class_names[np.argmax(prediction[i])])
plt.show()10000/10000 [==============================] - 1s 79us/sample - loss: 0.3884 - acc: 0.8587
Accuracy: 0.8587
