add lab07 solution

This commit is contained in:
michalzareba 2021-05-10 21:10:41 +02:00
parent b18e8dbb25
commit e29e65f1bc
7 changed files with 86 additions and 2 deletions

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@ -13,3 +13,4 @@ RUN pip3 install -r ./requirements.txt
CMD python3 script2.py CMD python3 script2.py
CMD python3 lab05.py CMD python3 lab05.py
RUN pip3 install gast==0.2.2 RUN pip3 install gast==0.2.2
CMD python3 lab07_sacred.py

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@ -29,6 +29,7 @@ pipeline {
stage('archiveArtifacts') { stage('archiveArtifacts') {
steps { steps {
archiveArtifacts 'eval.txt' archiveArtifacts 'eval.txt'
archiveArtifacts 'lab07/**'
} }
} }
stage('sendMail') { stage('sendMail') {

81
lab07_sacred.py Normal file
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@ -0,0 +1,81 @@
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.callbacks import EarlyStopping
from datetime import datetime
from sacred.observers import FileStorageObserver
from sacred import Experiment
ex = Experiment("file_observer", interactive=False)
ex.observers.append(FileStorageObserver('lab07/my_runs'))
@ex.config
def my_config():
train_size_param = 0.8
test_size_param = 0.2
epochs = 400
batch_size = 128
@ex.capture
def prepare_model(train_size_param, test_size_param, epochs, batch_size, _run):
_run.info["prepare_model_ts"] = str(datetime.now())
movies_data = pd.read_csv('train.csv', error_bad_lines=False)
movies_data.drop(movies_data.columns[0], axis=1, inplace=True)
movies_data.dropna(inplace=True)
X = movies_data.drop("rating", axis=1)
Y = movies_data["rating"]
print(X, Y.values)
# Split set to train/test 8:2 ratio
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=test_size_param, random_state=42
)
test_df = pd.read_csv('test.csv')
test_df.drop(test_df.columns[0], axis=1, inplace=True)
x_test = test_df.drop("rating", axis=1)
y_test = test_df["rating"]
# Set up model
model = Sequential()
model.add(Dense(8, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(3, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1))
model.compile(optimizer="adam", loss="mse")
early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
model.fit(
x=X_train.values,
y=Y_train.values,
validation_data=(X_test, Y_test.values),
batch_size=batch_size,
epochs=epochs,
callbacks=[early_stop],
)
y_pred = model.predict(x_test.values)
rmse = mean_squared_error(y_test, y_pred)
_run.info["Final Results: "] = rmse
model.save('model_movies')
return rmse
@ex.automain
def my_main(train_size_param, test_size_param, epochs, batch_size):
print(prepare_model())
r = ex.run()
ex.add_artifact("model_movies/saved_model.pb")

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@ -7,3 +7,4 @@ pandas==1.0.1
tensorflow==2.0.0b1 tensorflow==2.0.0b1
wget==3.2 wget==3.2
gast==0.3.3 gast==0.3.3
sacred==0.8.2