From 1e4571d5d364b19c9451d217ce0d956cb251846a Mon Sep 17 00:00:00 2001 From: Aditya Agashe Date: Fri, 25 Oct 2019 17:41:36 +0530 Subject: [PATCH] CNN Using TensorFlow --- Convolutional Neural Network/CNNtf.py | 30 +++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 Convolutional Neural Network/CNNtf.py diff --git a/Convolutional Neural Network/CNNtf.py b/Convolutional Neural Network/CNNtf.py new file mode 100644 index 0000000..884b82d --- /dev/null +++ b/Convolutional Neural Network/CNNtf.py @@ -0,0 +1,30 @@ + +import tensorflow as tf +import matplotlib.pyplot as plt +#plt.imshow(x_train[0]) +plt.imshow(x_train[0],cmap=plt.cm.binary) +plt.show() +print(x_test[0]) + +mnist = tf.keras.datasets.mnist # 28x28 images of handwritten digits from 0-9 +(x_train,y_train),(x_test,y_test) = mnist.load_data() # distibution labels of input data + +x_train = tf.keras.utils.normalize(x_train,axis=1) # normalization of training data +x_test = tf.keras.utils.normalize(x_test,axis=1) # normalization of testing data + +model = tf.keras.models.Sequential() +model.add(tf.keras.layers.Flatten()) +model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) +model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) +model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax)) + + +model.compile (optimizer='adam', + loss="sparse_categorical_crossentropy", + metrics =['accuracy'] + ) + +model.fit(x_train,y_train,epochs=3) + +val_loss, val_accuracy = model.evaluate(x_test,y_test) +print(val_loss,val_accuracy)