Upload quantumjump_195.py

quantumjump_195.py

@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+"""quantumjump.195
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1ajVqXJvko89LMeo0a9e_uB75te2fLwMY
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+import tensorflow as tf
+from tensorflow import keras
+import matplotlib.pyplot as plt
+# %matplotlib inline
+import numpy as np
+
+(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()
+
+len(X_train)
+
+len(X_test)
+
+X_train[0].shape
+
+X_train[0]
+
+plt.matshow(X_train[2])
+
+y_train[2]
+
+y_train[:5]
+
+X_train.shape
+
+X_train = X_train / 255
+X_test = X_test / 255
+
+X_train[0]
+
+X_train_flattened = X_train.reshape(len(X_train),28*28)
+X_test_flattened = X_test.reshape(len(X_test),28*28)
+
+X_train_flattened.shape
+
+X_train_flattened[0]
+
+model = keras.Sequential([
+    keras.layers.Dense(10, input_shape=(784,),activation='sigmoid')
+])
+
+model.compile(
+    optimizer='adam',
+    loss='sparse_categorical_crossentropy',
+    metrics=['accuracy']
+)
+model.fit(X_train_flattened, y_train, epochs=5)
+
+model.evaluate(X_test_flattened, y_test)
+
+plt.matshow(X_test[0])
+
+y_predicted = model.predict(X_test_flattened)
+y_predicted[0]
+
+np.argmax(y_predicted[1])
+
+y_predicted_labels = [np.argmax(i) for i in y_predicted]
+y_predicted_labels[:5]
+
+y_test[:5]
+
+cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)
+cm
+
+import seaborn as sn
+plt.figure(figsize = (10,7))
+sn.heatmap(cm, annot=True, fmt='d')
+plt.xlabel('Predicted')
+plt.ylabel('Truth')
+
+model = keras.Sequential([
+  keras.layers.Dense(100, input_shape=(784,), activation='relu'),
+  keras.layers.Dense(10, activation='sigmoid')
+])
+
+model.compile(
+  optimizer='adam',
+  loss='sparse_categorical_crossentropy',
+  metrics=['accuracy']
+)
+model.fit(X_train_flattened, y_train, epochs=5)
+
+model.evaluate(X_test_flattened,y_test)
+
+y_predicted = model.predict(X_test_flattened)
+y_predicted_labels = [np.argmax(i) for i in y_predicted]
+cm = tf.math.confusion_matrix(labels=y_test, predictions=y_predicted_labels)
+
+plt.figure(figsize = (10,7))
+sn.heatmap(cm, annot=True, fmt='d')
+plt.xlabel('Predicted')
+plt.ylabel('Truth')
+
+model = keras.Sequential([
+    keras.layers.Dense(100, input_shape=(784,),activation='relu'),
+    keras.layers.Dense(10, activation='sigmoid')
+])
+
+model.compile(
+  optimizer = 'adam',
+  loss='sparse_categorical_crossentropy',
+  metrics=['accuracy']
+)
+
+model.fit(X_train_flattened, y_train, epochs=5)