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Update deprecated code snippets in ZH-CN translations #738

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Update deprecated code snippets in ZH-CN translations #738

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22 changes: 11 additions & 11 deletions site/zh-cn/federated/tutorials/custom_federated_algorithms_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -287,7 +287,7 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90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\nrkJggg==\n",
"image/png": "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",
"text/plain": [
"<Figure size 600x400 with 1 Axes>"
]
Expand Down Expand Up @@ -353,7 +353,7 @@
"BATCH_SPEC = collections.OrderedDict(\n",
" x=tf.TensorSpec(shape=[None, 784], dtype=tf.float32),\n",
" y=tf.TensorSpec(shape=[None], dtype=tf.int32))\n",
"BATCH_TYPE = tff.to_type(BATCH_SPEC)\n",
"BATCH_TYPE = tff.types.tensorflow_to_type(BATCH_SPEC)\n",
"\n",
"str(BATCH_TYPE)"
]
Expand Down Expand Up @@ -388,7 +388,7 @@
"MODEL_SPEC = collections.OrderedDict(\n",
" weights=tf.TensorSpec(shape=[784, 10], dtype=tf.float32),\n",
" bias=tf.TensorSpec(shape=[10], dtype=tf.float32))\n",
"MODEL_TYPE = tff.to_type(MODEL_SPEC)\n",
"MODEL_TYPE = tff.types.tensorflow_to_type(MODEL_SPEC)\n",
"print(MODEL_TYPE)"
]
},
Expand Down Expand Up @@ -527,7 +527,7 @@
},
"outputs": [],
"source": [
"@tff.tf_computation(MODEL_TYPE, BATCH_TYPE, tf.float32)\n",
"@tff.tensorflow.computation(MODEL_TYPE, BATCH_TYPE, np.float32)\n",
"def batch_train(initial_model, batch, learning_rate):\n",
" # Define a group of model variables and set them to `initial_model`. Must\n",
" # be defined outside the @tf.function.\n",
Expand Down Expand Up @@ -644,11 +644,11 @@
"source": [
"LOCAL_DATA_TYPE = tff.SequenceType(BATCH_TYPE)\n",
"\n",
"@tff.federated_computation(MODEL_TYPE, tf.float32, LOCAL_DATA_TYPE)\n",
"@tff.federated_computation(MODEL_TYPE, np.float32, LOCAL_DATA_TYPE)\n",
"def local_train(initial_model, learning_rate, all_batches):\n",
" \n",
"\n",
" # Reduction function to apply to each batch.\n",
" @tff.federated_computation((MODEL_TYPE, tf.float32), BATCH_TYPE)\n",
" @tff.federated_computation((MODEL_TYPE, np.float32), BATCH_TYPE)\n",
" def batch_fn(model_with_lr, batch):\n",
" model, lr = model_with_lr\n",
" return batch_train(model, batch, lr), lr\n",
Expand Down Expand Up @@ -745,7 +745,7 @@
"@tff.federated_computation(MODEL_TYPE, LOCAL_DATA_TYPE)\n",
"def local_eval(model, all_batches):\n",
"\n",
" @tff.tf_computation((MODEL_TYPE, tf.float32), BATCH_TYPE)\n",
" @tff.tensorflow.computation((MODEL_TYPE, np.float32), BATCH_TYPE)\n",
" def accumulate_evaluation(model_and_accumulator, batch):\n",
" model, accumulator = model_and_accumulator\n",
" return model, accumulator + batch_loss(model, batch)\n",
Expand Down Expand Up @@ -881,8 +881,8 @@
},
"outputs": [],
"source": [
"SERVER_MODEL_TYPE = tff.type_at_server(MODEL_TYPE)\n",
"CLIENT_DATA_TYPE = tff.type_at_clients(LOCAL_DATA_TYPE)"
"SERVER_MODEL_TYPE = tff.FederatedType(MODEL_TYPE, tff.SERVER)\n",
"CLIENT_DATA_TYPE = tff.FederatedType(LOCAL_DATA_TYPE, tff.CLIENTS)"
]
},
{
Expand Down Expand Up @@ -984,7 +984,7 @@
},
"outputs": [],
"source": [
"SERVER_FLOAT_TYPE = tff.type_at_server(tf.float32)\n",
"SERVER_FLOAT_TYPE = tff.FederatedType(np.float32, tff.SERVER)\n",
"\n",
"\n",
"@tff.federated_computation(SERVER_MODEL_TYPE, SERVER_FLOAT_TYPE,\n",
Expand Down