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BinarySandia04
2026-05-21 15:05:51 +02:00
parent 6a25385409
commit 3162934b99
2 changed files with 5 additions and 4 deletions
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3
run.sh
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@@ -16,7 +16,8 @@ source "$VENV_DIR/bin/activate"
# 3. Install dependencies (lightweight, safe to re-run) # 3. Install dependencies (lightweight, safe to re-run)
echo "Installing dependencies..." echo "Installing dependencies..."
pip install --upgrade pip pip install --upgrade pip
pip install pandas numpy matplotlib seaborn pillow scikit-learn tensorflow pip install pandas numpy matplotlib seaborn pillow scikit-learn
pip install install "tensorflow[and-cuda]"
pip install --upgrade kagglehub[pandas-datasets,hf-datasets] pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
python3 skin_cancer_classification.py python3 skin_cancer_classification.py

6
skin_cancer_classification.py
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@@ -271,9 +271,9 @@ x = Dense(512, activation='relu')(x) # Added another Dense layer
x = Dense(256, activation='relu')(x) # Existing Dense layer x = Dense(256, activation='relu')(x) # Existing Dense layer
predictions = Dense(1, activation='sigmoid')(x) # Output layer for binary classification predictions = Dense(1, activation='sigmoid')(x) # Output layer for binary classification
# with strategy.scope(): # Use all gpus with strategy.scope(): # Use all gpus
model = Model(inputs=base_model.input, outputs=predictions) model = Model(inputs=base_model.input, outputs=predictions)
model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy']) model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
"""## 4. Data Generators """## 4. Data Generators