Repository created

Author: mansijpackt

Activity01/mnist.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[5]:
+
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Convolution2D, Flatten, Dense, Dropout
+import datetime
+
+from keras.optimizers import adam
+
+
+# In[17]:
+
+
+mnist = tf.keras.datasets.mnist
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+X_train, X_test = X_train / 255.0, X_test / 255.0
+
+
+# In[20]:
+
+log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+
+X_train = X_train.reshape(X_train.shape[0],X_train.shape[1], X_train.shape[2],1)
+X_test = X_test.reshape(X_test.shape[0],X_test.shape[1], X_test.shape[2],1)
+
+
+# In[21]:
+
+
+model = Sequential()
+model.add(Convolution2D(filters = 10, kernel_size = 3, input_shape=(28,28,1)))
+model.add(Flatten())
+model.add(Dense(128, activation = 'relu'))
+model.add(Dropout(0.2))
+model.add(Dense(10, activation = 'softmax'))
+
+
+
+
+# In[22]:
+
+learning_rate=0.001
+
+
+model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = learning_rate),
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+
+# In[23]:
+
+
+model.fit(X_train, y_train, epochs=5, validation_data=(X_test, y_test),callbacks=[tensorboard_callback])
+
+
+# In[ ]:
+
+
+
+

Activity01/requirements.txt

@@ -0,0 +1,99 @@
+absl-py==0.8.1
+astor==0.8.0
+attrs==19.3.0
+backcall==0.1.0
+beautifulsoup4==4.8.1
+bleach==3.1.0
+cachetools==3.1.1
+certifi==2019.9.11
+chardet==3.0.4
+Click==7.0
+cycler==0.10.0
+decorator==4.4.1
+defusedxml==0.6.0
+entrypoints==0.3
+Flask==1.1.1
+Flask-API==2.0
+Flask-Caching==1.8.0
+Flask-Cors==3.0.8
+Flask-Testing==0.7.1
+gast==0.2.2
+google-auth==1.7.0
+google-auth-oauthlib==0.4.1
+google-pasta==0.1.8
+graphviz==0.13.2
+grpcio==1.24.3
+h5py==2.10.0
+html5lib==1.0.1
+idna==2.8
+importlib-metadata==1.3.0
+ipykernel==5.1.3
+ipython==7.10.2
+ipython-genutils==0.2.0
+ipywidgets==7.5.1
+itsdangerous==1.1.0
+jedi==0.15.2
+Jinja2==2.10.3
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==5.3.4
+jupyter-console==6.0.0
+jupyter-core==4.6.1
+Keras==2.2.4
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+kiwisolver==1.1.0
+Markdown==3.1.1
+MarkupSafe==1.1.1
+matplotlib==3.1.2
+mistune==0.8.4
+more-itertools==8.0.2
+nbconvert==5.6.1
+nbformat==4.4.0
+notebook==6.0.2
+numpy==1.17.3
+oauthlib==3.1.0
+opt-einsum==3.1.0
+pandas==0.25.3
+pandocfilters==1.4.2
+parso==0.5.2
+pexpect==4.7.0
+pickleshare==0.7.5
+prometheus-client==0.7.1
+prompt-toolkit==2.0.10
+protobuf==3.10.0
+ptyprocess==0.6.0
+pyasn1==0.4.7
+pyasn1-modules==0.2.7
+Pygments==2.5.2
+pyparsing==2.4.5
+pyrsistent==0.15.6
+python-dateutil==2.8.1
+pytz==2019.3
+PyYAML==5.2
+pyzmq==18.1.1
+qtconsole==4.6.0
+requests==2.22.0
+requests-oauthlib==1.2.0
+rsa==4.0
+scipy==1.4.1
+seaborn==0.9.0
+Send2Trash==1.5.0
+six==1.13.0
+soupsieve==1.9.5
+tensorboard==2.0.1
+tensorflow==2.0.0
+tensorflow-estimator==2.0.1
+termcolor==1.1.0
+terminado==0.8.3
+testpath==0.4.4
+tornado==6.0.3
+tqdm==4.41.0
+traitlets==4.3.3
+urllib3==1.25.6
+wcwidth==0.1.7
+webencodings==0.5.1
+Werkzeug==0.16.0
+widgetsnbextension==3.5.1
+wrapt==1.11.2
+zipp==0.6.0

Exercise01/requirements.txt

@@ -0,0 +1,100 @@
+absl-py==0.8.1
+astor==0.8.0
+attrs==19.3.0
+backcall==0.1.0
+beautifulsoup4==4.8.1
+bleach==3.1.0
+cachetools==3.1.1
+certifi==2019.9.11
+chardet==3.0.4
+Click==7.0
+cycler==0.10.0
+decorator==4.4.1
+defusedxml==0.6.0
+entrypoints==0.3
+Flask==1.1.1
+Flask-API==2.0
+Flask-Caching==1.8.0
+Flask-Cors==3.0.8
+Flask-Testing==0.7.1
+gast==0.2.2
+google-auth==1.7.0
+google-auth-oauthlib==0.4.1
+google-pasta==0.1.8
+graphviz==0.13.2
+grpcio==1.24.3
+h5py==2.10.0
+html5lib==1.0.1
+idna==2.8
+importlib-metadata==1.3.0
+ipykernel==5.1.3
+ipython==7.10.2
+ipython-genutils==0.2.0
+ipywidgets==7.5.1
+itsdangerous==1.1.0
+jedi==0.15.2
+Jinja2==2.10.3
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==5.3.4
+jupyter-console==6.0.0
+jupyter-core==4.6.1
+Keras==2.2.4
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+kiwisolver==1.1.0
+Markdown==3.1.1
+MarkupSafe==1.1.1
+matplotlib==3.1.2
+mistune==0.8.4
+more-itertools==8.0.2
+nbconvert==5.6.1
+nbformat==4.4.0
+notebook==6.0.2
+numpy==1.17.3
+oauthlib==3.1.0
+opt-einsum==3.1.0
+pandas==0.25.3
+pandocfilters==1.4.2
+parso==0.5.2
+pexpect==4.7.0
+pickleshare==0.7.5
+prometheus-client==0.7.1
+prompt-toolkit==2.0.10
+protobuf==3.10.0
+ptyprocess==0.6.0
+pyasn1==0.4.7
+pyasn1-modules==0.2.7
+Pygments==2.5.2
+pyparsing==2.4.5
+pyrsistent==0.15.6
+python-dateutil==2.8.1
+pytz==2019.3
+PyYAML==5.2
+pyzmq==18.1.1
+qtconsole==4.6.0
+requests==2.22.0
+requests-oauthlib==1.2.0
+rsa==4.0
+scipy==1.4.1
+seaborn==0.9.0
+Send2Trash==1.5.0
+setuptools==41.0.0
+six==1.13.0
+soupsieve==1.9.5
+tensorboard==2.0.1
+tensorflow==2.0.0
+tensorflow-estimator==2.0.1
+termcolor==1.1.0
+terminado==0.8.3
+testpath==0.4.4
+tornado==6.0.3
+tqdm==4.41.0
+traitlets==4.3.3
+urllib3==1.25.6
+wcwidth==0.1.7
+webencodings==0.5.1
+Werkzeug==0.16.0
+widgetsnbextension==3.5.1
+wrapt==1.11.2
+zipp==0.6.0

Exercise02/mnist.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[5]:
+
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Convolution2D, Flatten, Dense, Dropout
+import datetime
+
+# In[17]:
+
+
+mnist = tf.keras.datasets.mnist
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+X_train, X_test = X_train / 255.0, X_test / 255.0
+
+
+# In[20]:
+
+log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+
+X_train = X_train.reshape(X_train.shape[0],X_train.shape[1], X_train.shape[2],1)
+X_test = X_test.reshape(X_test.shape[0],X_test.shape[1], X_test.shape[2],1)
+
+
+# In[21]:
+
+
+model = Sequential()
+model.add(Convolution2D(filters = 10, kernel_size = 3, input_shape=(28,28,1)))
+model.add(Flatten())
+model.add(Dense(128, activation = 'relu'))
+model.add(Dropout(0.2))
+model.add(Dense(10, activation = 'softmax'))
+
+
+# In[22]:
+
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+
+# In[23]:
+
+
+model.fit(X_train, y_train, epochs=5, validation_data=(X_test, y_test),callbacks=[tensorboard_callback])
+
+
+# In[ ]:
+
+
+
+