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FLOPS\uff08\u6bcf\u79d2\u6d6e\u70b9\u8fd0\u7b97\u6b21\u6570\uff09\u3002\u8fd9\u4e2a\u5929\u6587\u6570\u5b57\u8d85\u51fa\u4e86\u4f20\u7edf\u8ba1\u7b97\u8bbe\u7f6e\u7684\u80fd\u529b\u8303\u56f4\u3002\u800c\u4e91\u5316\u7b97\u529b\u53ef\u4ee5\u5229\u7528\u6210\u5343\u4e0a\u4e07\u670d\u52a1\u5668\u7684\u96c6\u4f53\u529b\u91cf\u6765\u5b8c\u6210\u5982\u6b64\u8270\u5de8\u7684\u4efb\u52a1\u3002 [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] \u4e91\u4e0e\u4eba\u5de5\u667a\u80fd\u7684\u5171\u751f\uff1a\u6280\u672f\u6df1\u5ea6\u63a2\u8ba8 \u8981\u771f\u6b63\u7406\u89e3\u4e91\u5316\u7b97\u529b\u548c\u4eba\u5de5\u667a\u80fd\u4e4b\u95f4\u7684\u534f\u540c\u4f5c\u7528\uff0c\u8ba9\u6211\u4eec\u6765\u770b\u4e00\u4e2a\u5b9e\u9645\u7684\u4f8b\u5b50\u3002\u5047\u8bbe\u6211\u4eec\u6b63\u5728\u5f00\u53d1\u4e00\u4e2a\u8ba1\u7b97\u673a\u89c6\u89c9\u6a21\u578b\uff0c\u7528\u4e8e\u5728\u5929\u6587\u6570\u636e\u4e2d\u68c0\u6d4b\u7f55\u89c1\u7684\u5929\u4f53\u4e8b\u4ef6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5229\u7528\u4e91\u8d44\u6e90\u7684\u7b80\u5316Python\u811a\u672c\uff1a import tensorflow as tf from tensorflow.keras import layers from google.cloud import storage # Initialize cloud storage client storage_client = storage.Client() # Define model architecture model = tf.keras.Sequential([ layers.Conv2D(32, 3, activation=’relu’, input_shape=(256, 256, 3)), layers.MaxPooling2D(), layers.Conv2D(64, 3, activation=’relu’), layers.MaxPooling2D(), layers.Conv2D(64, 3, activation=’relu’), layers.Flatten(), […]<\/p>\n\u89e3\u7801\u4e91\u5316\u7b97\u529b\uff1a\u4eba\u5de5\u667a\u80fd\u5f15\u64ce\u7684\u71c3\u6599<\/strong><\/h2>\n
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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\u4e91\u5316\u7b97\u529b\u548c\u4eba\u5de5\u667a\u80fd\u521b\u65b0\u7684\u67a2\u7ebd<\/strong><\/h2>\n
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