Trying example from Comet.ml (Keras)

Part 2 - Regression/Section 5 - Multiple Linear Regression/multiple_linear_regression.py

@@ -4,6 +4,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
+from sklearn.metrics import mean_squared_error
 
 # Importing the dataset
 dataset = pd.read_csv('50_Startups.csv')
@@ -57,4 +58,12 @@
 regressor_OLS.summary()
 X_opt = X[:, [0, 3]]
 regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
-regressor_OLS.summary()
+regressor_OLS.summary()
+
+mse1 = mean_squared_error(y_test, y_pred)
+
+X_train, X_test, y_train, y_test = train_test_split(X_opt, y, test_size = 0.2, random_state = 0)
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+y_pred = regressor.predict(X_test)
+mse2 = mean_squared_error(y_test, y_pred)

Part 8 - Deep Learning/Section 39 - Artificial Neural Networks (ANN)/Artificial_Neural_Networks/ann.py

@@ -9,12 +9,36 @@
 # Installing Keras
 # pip install --upgrade keras
 
+from comet_ml import Experiment
+
+#create an experiment with your api key
+experiment = Experiment(api_key="Dz2W3DAahv0OvSAERUfhA5b7I",
+                        project_name='general',
+                        auto_param_logging=False)
+
+batch_size = 10
+epochs = 50
+num_nodes = 6
+activation = 'relu'
+optimizer = 'adam'
+
+#these will all get logged 
+params={'batch_size':batch_size,
+        'epochs':epochs,
+        'layer1_type':'Dense',
+        'layer1_num_nodes':num_nodes,
+        'layer1_activation':activation,
+        'optimizer':optimizer
+}
+
+
 # Part 1 - Data Preprocessing
 
 # Importing the libraries
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
+from keras.callbacks import EarlyStopping
 
 # Importing the dataset
 dataset = pd.read_csv('Churn_Modelling.csv')
@@ -60,18 +84,40 @@
 # Adding the output layer
 classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
 
+#print model.summary() to preserve automatically in `Output` tab
+print(classifier.summary())
+
 # Compiling the ANN
 classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
 
-# Fitting the ANN to the Training set
-classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)
+
+#will log metrics with the prefix 'train_'
+with experiment.train():
+    # Fitting the ANN to the Training set
+    classifier.fit(X_train, y_train,
+                   batch_size = batch_size,
+                   nb_epoch = epochs,
+                   verbose = 1,
+                   callbacks=[EarlyStopping(monitor='val_loss', min_delta=1e-4,patience=3, verbose=1, mode='auto')])
 
 # Part 3 - Making the predictions and evaluating the model
 
 # Predicting the Test set results
-y_pred = classifier.predict(X_test)
-y_pred = (y_pred > 0.5)
+    y_pred = classifier.predict(X_test)
+    y_pred = (y_pred > 0.5)
+
+#will log metrics with the prefix 'test_'
+with experiment.test():
+    loss, accuracy = classifier.evaluate(X_test, y_test)
+    metrics = {
+        'loss':loss,
+        'accuracy':accuracy
+    }
+    experiment.log_multiple_metrics(metrics)
 
 # Making the Confusion Matrix
 from sklearn.metrics import confusion_matrix
-cm = confusion_matrix(y_test, y_pred)
+cm = confusion_matrix(y_test, y_pred)
+
+experiment.log_multiple_params(params)
+experiment.log_dataset_hash(X_train) #creates and logs a hash of your data

Part 8 - Deep Learning/Section 39 - Artificial Neural Networks (ANN)/Artificial_Neural_Networks/temp_comet.py

@@ -0,0 +1,77 @@
+from comet_ml import Experiment
+
+#create an experiment with your api key
+experiment = Experiment(api_key="Dz2W3DAahv0OvSAERUfhA5b7I",
+                        project_name='general',
+                        auto_param_logging=False)
+
+import keras
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense, Dropout
+from keras.callbacks import EarlyStopping
+
+batch_size = 16
+num_classes = 10
+epochs = 5
+num_nodes = 16
+optimizer = 'adam'
+activation = 'relu'
+
+# the data, shuffled and split between train and test sets
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+x_train = x_train.reshape(60000, 784)
+x_test = x_test.reshape(10000, 784)
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+x_train /= 255
+x_test /= 255
+print(x_train.shape[0], 'train samples')
+print(x_test.shape[0], 'test samples')
+
+
+# convert class vectors to binary class matrices
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+#these will all get logged 
+params={'batch_size':batch_size,
+        'epochs':epochs,
+        'layer1_type':'Dense',
+        'layer1_num_nodes':num_nodes,
+        'layer1_activation':activation,
+        'optimizer':optimizer
+}
+model = Sequential()
+model.add(Dense(num_nodes, activation='relu', input_shape=(784,)))
+#model.add(Dense(256, activation='relu'))
+model.add(Dense(num_classes, activation='softmax'))
+
+#print model.summary() to preserve automatically in `Output` tab
+print(model.summary())
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=optimizer,
+              metrics=['accuracy'])
+
+#will log metrics with the prefix 'train_'
+with experiment.train():
+    history = model.fit(x_train, y_train,
+                        batch_size=batch_size,
+                        epochs=epochs,
+                        verbose=1,
+                        validation_data=(x_test, y_test),
+                        callbacks=[EarlyStopping(monitor='val_loss', min_delta=1e-4,patience=3, verbose=1, mode='auto')])
+
+#will log metrics with the prefix 'test_'
+with experiment.test():
+    loss, accuracy = model.evaluate(x_test, y_test)
+    metrics = {
+        'loss':loss,
+        'accuracy':accuracy
+    }
+    experiment.log_multiple_metrics(metrics)
+
+experiment.log_multiple_params(params)
+experiment.log_dataset_hash(x_train) #creates and logs a hash of your data