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import tensorflow as tf\r\nfrom tensorflow.keras import layers\r\nfrom google.cloud import storage\r\n\r\n# Initialize cloud storage client\r\nstorage_client = storage.Client()\r\n\r\n# Define model architecture\r\nmodel = tf.keras.Sequential([\r\n layers.Conv2D(32, 3, activation='relu', input_shape=(256, 256, 3)),\r\n layers.MaxPooling2D(),\r\n layers.Conv2D(64, 3, activation='relu'),\r\n layers.MaxPooling2D(),\r\n layers.Conv2D(64, 3, activation='relu'),\r\n layers.Flatten(),\r\n layers.Dense(64, activation='relu'),\r\n layers.Dense(1, activation='sigmoid')\r\n])\r\n\r\n# Compile model\r\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\r\n\r\n# Load data from cloud storage\r\nbucket = storage_client.get_bucket('astronomical_data')\r\nblob = bucket.blob('training_data.npz')\r\nblob.download_to_filename('\/tmp\/training_data.npz')\r\n\r\ndata = np.load('\/tmp\/training_data.npz')\r\nX_train, y_train = data['X'], data['y']\r\n\r\n# Train model\r\nmodel.fit(X_train, y_train, epochs=50, batch_size=32)\r\n\r\n# Save model to cloud storage\r\nmodel.save('\/tmp\/celestial_event_model.h5')\r\nblob = bucket.blob('models\/celestial_event_model.h5')\r\nblob.upload_from_filename('\/tmp\/celestial_event_model.h5')\r\n<\/code><\/pre>\n\u9999\u6e2f\uff1a\u96f2\u5316\u7b97\u529b\u548c\u4eba\u5de5\u667a\u6167\u5275\u65b0\u7684\u6a1e\u7d10<\/strong><\/h2>\n
\n
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