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ItsukiNakano

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import callbacks
from sklearn.model_selection import train_test_split, KFold, StratifiedKFold, cross_val_score
from sklearn.metrics import accuracy_score
from keras.utils import np_utils
from sklearn.linear_model import LogisticRegression
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
from sklearn.preprocessing import LabelEncoder, StandardScaler
from tensorflow.keras.preprocessing.image import ImageDataGenerator

data_dir = '/content/drive/MyDrive/Colab Notebooks/Multi-class Weather Dataset'

filepaths, labels = [], []

for folder in os.listdir(data_dir):
folder_path = os.path.join(data_dir, folder)
for file in os.listdir(folder_path):
file_path = os.path.join(folder_path, file)
filepaths.append(file_path)
labels.append(folder)

df = pd.DataFrame({'filepaths': filepaths, 'labels': labels})


# train
train_df, dummy_df = train_test_split(df, train_size= 0.8, shuffle= True, random_state= 42)

# valid and test
valid_df, test_df = train_test_split(dummy_df, train_size= 0.5, shuffle= True, random_state= 42)


batch_size = 32
img_size = 224
channels = 3
img_shape = (img_size, img_size, channels)

train_gen = ImageDataGenerator()
test_gen = ImageDataGenerator()

train_gen = train_gen.flow_from_dataframe(train_df, x_col='filepaths', y_col='labels',
target_size=(img_size, img_size), class_mode='categorical',
batch_size=batch_size, shuffle=True)

valid_gen = test_gen.flow_from_dataframe(valid_df, x_col='filepaths', y_col='labels',
target_size=(img_size, img_size), class_mode='categorical',
batch_size=batch_size, shuffle=True)

ts_length = len(test_df)
test_batch_size = max(sorted([ts_length // n for n in range(1, ts_length + 1) if ts_length%n == 0 and ts_length/n <= 80]))
test_steps = ts_length // test_batch_size

test_gen = test_gen.flow_from_dataframe(test_df, x_col='filepaths', y_col='labels',
target_size=(img_size, img_size), class_mode='categorical',
batch_size=test_batch_size, shuffle=False)


there are 4 image classification types on my dataset, Create a baseline MLP classification model
 
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Sure, I can help you with that!

To create a baseline MLP classification model for your image dataset, you can follow these steps:

1. Import the necessary libraries:

Code: 
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

2. Define the model architecture:

Code: 
model = Sequential([
    layers.Flatten(input_shape=img_shape),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(4, activation='softmax')
])

3. Compile the model:

Code: 
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

4. Train the model:

Code: 
history = model.fit(train_gen, epochs=10, validation_data=valid_gen)

5. Evaluate the model on the test set:

Code: 
test_loss, test_acc = model.evaluate(test_gen, steps=test_steps)

Here's the complete code:

Code: 
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

# Define the model architecture
model = Sequential([
    layers.Flatten(input_shape=img_shape),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(4, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(train_gen, epochs=10, validation_data=valid_gen)

# Evaluate the model on the test set
test_loss, test_acc = model.evaluate(test_gen, steps=test_steps)

You can adjust the number of neurons, layers, and epochs to find the best model performance for your dataset. Good luck!
 
Upvote 0 Downvote
🤖 said:
Sure, I can help you with that!

To create a baseline MLP classification model for your image dataset, you can follow these steps:

1. Import the necessary libraries:

Code: 
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

2. Define the model architecture:

Code: 
model = Sequential([
    layers.Flatten(input_shape=img_shape),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(4, activation='softmax')
])

3. Compile the model:

Code: 
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

4. Train the model:

Code: 
history = model.fit(train_gen, epochs=10, validation_data=valid_gen)

5. Evaluate the model on the test set:

Code: 
test_loss, test_acc = model.evaluate(test_gen, steps=test_steps)

Here's the complete code:

Code: 
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

# Define the model architecture
model = Sequential([
    layers.Flatten(input_shape=img_shape),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(4, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(train_gen, epochs=10, validation_data=valid_gen)

# Evaluate the model on the test set
test_loss, test_acc = model.evaluate(test_gen, steps=test_steps)

You can adjust the number of neurons, layers, and epochs to find the best model performance for your dataset. Good luck!
Click to expand...
integrate it to the given code above
 
Upvote 0 Downvote
ItsukiNanako said:
integrate it to the given code above
Click to expand...
Sure! Here's how you can integrate the baseline MLP classification model to the code you provided:

Code: 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import callbacks
from sklearn.model_selection import train_test_split, KFold, StratifiedKFold, cross_val_score
from sklearn.metrics import accuracy_score
from keras.utils import np_utils
from sklearn.linear_model import LogisticRegression
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
from sklearn.preprocessing import LabelEncoder, StandardScaler
from tensorflow.keras.preprocessing.image import ImageDataGenerator

data_dir = '/content/drive/MyDrive/Colab Notebooks/Multi-class Weather Dataset'

filepaths, labels = [], []

for folder in os.listdir(data_dir):
    folder_path = os.path.join(data_dir, folder)
    for file in os.listdir(folder_path):
        file_path = os.path.join(folder_path, file)
        filepaths.append(file_path)
        labels.append(folder)

df = pd.DataFrame({'filepaths': filepaths, 'labels': labels})

# train
train_df, dummy_df = train_test_split(df, train_size=0.8, shuffle=True, random_state=42)

# valid and test
valid_df, test_df = train_test_split(dummy_df, train_size=0.5, shuffle=True, random_state=42)

batch_size = 32
img_size = 224
channels = 3
img_shape = (img_size, img_size, channels)

train_gen = ImageDataGenerator()
test_gen = ImageDataGenerator()

train_gen = train_gen.flow_from_dataframe(train_df, x_col='filepaths', y_col='labels', 
                                          target_size=(img_size, img_size), class_mode='categorical',
                                          batch_size=batch_size, shuffle=True)

valid_gen = test_gen.flow_from_dataframe(valid_df, x_col='filepaths', y_col='labels', 
                                          target_size=(img_size, img_size), class_mode='categorical',
                                          batch_size=batch_size, shuffle=True)

ts_length = len(test_df)
test_batch_size = max(sorted([ts_length // n for n in range(1, ts_length + 1) if ts_length % n == 0 and ts_length / n <= 80]))
test_steps = ts_length // test_batch_size

test_gen = test_gen.flow_from_dataframe(test_df, x_col='filepaths', y_col='labels', 
                                        target_size=(img_size, img_size), class_mode='categorical',
                                        batch_size=test_batch_size, shuffle=False)


# Define the model architecture
model = Sequential([
    layers.Flatten(input_shape=img_shape),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(4, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(train_gen, epochs=10, validation_data=valid_gen)

# Evaluate the model on the test set
test_loss, test_acc = model.evaluate(test_gen, steps=test_steps)

This should integrate the baseline MLP classification model to your existing code. Remember to adjust the hyperparameters as needed to improve model performance. Good luck!
 
Upvote 0 Downvote
🤖 said:
Sure! Here's how you can integrate the baseline MLP classification model to the code you provided:

Code: 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import callbacks
from sklearn.model_selection import train_test_split, KFold, StratifiedKFold, cross_val_score
from sklearn.metrics import accuracy_score
from keras.utils import np_utils
from sklearn.linear_model import LogisticRegression
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
from sklearn.preprocessing import LabelEncoder, StandardScaler
from tensorflow.keras.preprocessing.image import ImageDataGenerator

data_dir = '/content/drive/MyDrive/Colab Notebooks/Multi-class Weather Dataset'

filepaths, labels = [], []

for folder in os.listdir(data_dir):
    folder_path = os.path.join(data_dir, folder)
    for file in os.listdir(folder_path):
        file_path = os.path.join(folder_path, file)
        filepaths.append(file_path)
        labels.append(folder)

df = pd.DataFrame({'filepaths': filepaths, 'labels': labels})

# train
train_df, dummy_df = train_test_split(df, train_size=0.8, shuffle=True, random_state=42)

# valid and test
valid_df, test_df = train_test_split(dummy_df, train_size=0.5, shuffle=True, random_state=42)

batch_size = 32
img_size = 224
channels = 3
img_shape = (img_size, img_size, channels)

train_gen = ImageDataGenerator()
test_gen = ImageDataGenerator()

train_gen = train_gen.flow_from_dataframe(train_df, x_col='filepaths', y_col='labels',
                                          target_size=(img_size, img_size), class_mode='categorical',
                                          batch_size=batch_size, shuffle=True)

valid_gen = test_gen.flow_from_dataframe(valid_df, x_col='filepaths', y_col='labels',
                                          target_size=(img_size, img_size), class_mode='categorical',
                                          batch_size=batch_size, shuffle=True)

ts_length = len(test_df)
test_batch_size = max(sorted([ts_length // n for n in range(1, ts_length + 1) if ts_length % n == 0 and ts_length / n <= 80]))
test_steps = ts_length // test_batch_size

test_gen = test_gen.flow_from_dataframe(test_df, x_col='filepaths', y_col='labels',
                                        target_size=(img_size, img_size), class_mode='categorical',
                                        batch_size=test_batch_size, shuffle=False)


# Define the model architecture
model = Sequential([
    layers.Flatten(input_shape=img_shape),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(4, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(train_gen, epochs=10, validation_data=valid_gen)

# Evaluate the model on the test set
test_loss, test_acc = model.evaluate(test_gen, steps=test_steps)

This should integrate the baseline MLP classification model to your existing code. Remember to adjust the hyperparameters as needed to improve model performance. Good luck!
Click to expand...
make the baseline MLP classification model based on kfold
 
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ItsukiNanako said:
make the baseline MLP classification model based on kfold
Click to expand...
Sure, here's how you can modify the code to create a baseline MLP classification model based on K-fold cross-validation:

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import callbacks
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.preprocessing import LabelEncoder, StandardScaler
from tensorflow.keras.preprocessing.image import ImageDataGenerator

data_dir = '/content/drive/MyDrive/Colab Notebooks/Multi-class Weather Dataset'

filepaths, labels = [], []

for folder in os.listdir(data_dir):
folder_path = os.path.join(data_dir, folder)
for file in os.listdir(folder_path):
file_path = os.path.join(folder_path, file)
filepaths.append(file_path)
labels.append(folder)

df = pd.DataFrame({'filepaths': filepaths, 'labels': labels})

batch_size = 32
img_size = 224
channels = 3
img_shape = (img_size, img_size, channels)

train_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()

kfold = KFold(n_splits=5, shuffle=True, random_state=42)
scores = []

for train_idx, test_idx in kfold.split(df):
train_df = df.iloc[train_idx]
test_df = df.iloc[test_idx]

train_gen = train_datagen.flow_from_dataframe(train_df, x_col='filepaths', y_col='labels',
target_size=(img_size, img_size), class_mode='categorical',
batch_size=batch_size, shuffle=True)

test_gen = test_datagen.flow_from_dataframe(test_df, x_col='filepaths', y_col='labels',
target_size=(img_size, img_size), class_mode='categorical',
batch_size=batch_size, shuffle=False)

# Define the model architecture
model = Sequential([
layers.Flatten(input_shape=img_shape),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(4, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(train_gen, epochs=10, validation_data=test_gen)

# Evaluate the model on the test set
test_loss, test_acc = model.evaluate(test_gen)

scores.append(test
 
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fi
🤖 said:
Sure, here's how you can modify the code to create a baseline MLP classification model based on K-fold cross-validation:

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import callbacks
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.preprocessing import LabelEncoder, StandardScaler
from tensorflow.keras.preprocessing.image import ImageDataGenerator

data_dir = '/content/drive/MyDrive/Colab Notebooks/Multi-class Weather Dataset'

filepaths, labels = [], []

for folder in os.listdir(data_dir):
folder_path = os.path.join(data_dir, folder)
for file in os.listdir(folder_path):
file_path = os.path.join(folder_path, file)
filepaths.append(file_path)
labels.append(folder)

df = pd.DataFrame({'filepaths': filepaths, 'labels': labels})

batch_size = 32
img_size = 224
channels = 3
img_shape = (img_size, img_size, channels)

train_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()

kfold = KFold(n_splits=5, shuffle=True, random_state=42)
scores = []

for train_idx, test_idx in kfold.split(df):
train_df = df.iloc[train_idx]
test_df = df.iloc[test_idx]

train_gen = train_datagen.flow_from_dataframe(train_df, x_col='filepaths', y_col='labels',
target_size=(img_size, img_size), class_mode='categorical',
batch_size=batch_size, shuffle=True)

test_gen = test_datagen.flow_from_dataframe(test_df, x_col='filepaths', y_col='labels',
target_size=(img_size, img_size), class_mode='categorical',
batch_size=batch_size, shuffle=False)

# Define the model architecture
model = Sequential([
layers.Flatten(input_shape=img_shape),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(4, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(train_gen, epochs=10, validation_data=test_gen)

# Evaluate the model on the test set
test_loss, test_acc = model.evaluate(test_gen)

scores.append(test
Click to expand...
finish the code implementation
 
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