{"action":"created","comment":{"url":"https://api.github.com/repos/SpaceNinjaHQ/DeepLearningTutorials/pulls/comments/34649922","id":34649922,"diff_hunk":"@@ -0,0 +1,155 @@\n+\n+# Short routines to save, rebuild, and run neural network instances.\n+\n+import numpy\n+import cPickle\n+\n+import theano\n+import theano.tensor as T\n+import logistic_sgd\n+\n+### SAVE ###\n+\n+def save_layer(outfile,W,b):\n+    \"\"\"Save weights and biases for a single NN layer.\"\"\"\n+    with open(outfile,'wb') as of:\n+        cPickle.dump(W.get_value(borrow=True),of,-1)\n+        cPickle.dump(b.get_value(borrow=True),of,-1)\n+    return\n+\n+def save_params(file_prefix,params):\n+    \"\"\"Save weights and biases for all NN layers, one layer per file.\"\"\"\n+    weights = params[::2]\n+    biases = params[1::2]\n+    for layer,(W,b) in enumerate(zip(weights,biases)):\n+        outfile = file_prefix+'Layer'+str(layer)+'.pkl'\n+        save_layer(outfile,W,b)\n+    return\n+\n+def save_classifier(outfile,classifier):\n+    \"\"\"Save a classifier class instance.\"\"\"\n+    # WIP: Though this code is ripped from logistic_sgd.py, I haven't\n+    # been able to reload the pickled classifier.  (See\n+    # load_classifier(), below.)\n+    with open(outfile,'wb') as of:\n+        cPickle.dump(classifier, f)\n+    return\n+\n+### REBUILD ###\n+\n+def load_params(file):\n+    \"\"\"Load pickled weights and biases for a single NN layer.  Assumes weights\n+    and biases were pickled in sequence into the specified file.\n+    \"\"\"\n+    W = theano.shared(\n+        value=numpy.zeros(\n+            (0,0),\n+            dtype=theano.config.floatX\n+        ),\n+        name='W',\n+        borrow=True\n+    )\n+    b = theano.shared(\n+        value=numpy.zeros(\n+            (0,),\n+            dtype=theano.config.floatX\n+        ),\n+        name='b',\n+        borrow=True\n+    )\n+    with open(file,'rb') as inf:\n+        W.set_value(cPickle.load(inf), borrow=True)\n+        b.set_value(cPickle.load(inf), borrow=True)\n+    return W,b\n+\n+def load_W(Wfile):\n+    \"\"\"Load pickled weights for a single NN layer.\"\"\"\n+    W = theano.shared(\n+        value=numpy.zeros(\n+            (0,0),\n+            dtype=theano.config.floatX\n+        ),\n+        name='W',\n+        borrow=True\n+    )\n+    with open(Wfile,'rb') as inf:\n+        W.set_value(cPickle.load(inf), borrow=True)\n+    return W\n+\n+def load_b(bfile):\n+    \"\"\"Load pickled biases for a single NN layer.\"\"\"\n+    b = theano.shared(\n+        value=numpy.zeros(\n+            (0,),\n+            dtype=theano.config.floatX\n+        ),\n+        name='b',\n+        borrow=True\n+    )\n+    with open(bfile,'rb') as inf:\n+        b.set_value(cPickle.load(inf), borrow=True)\n+    return b\n+\n+def rebuild_logreg(param_file):\n+    \"\"\"Given pickled weights and biases for a logistic regression layer of an\n+    NN, rebuild the corresponding class instance.  Returns the class\n+    instance.\n+    \"\"\"\n+    W, b = load_params(param_file)\n+    x = T.matrix('x')\n+    lr = logistic_sgd.LogisticRegression(x,*W.get_value().shape)\n+    lr.W.set_value(W.get_value())\n+    lr.b.set_value(b.get_value())\n+    return lr\n+\n+def load_classifier(filename):\n+    \"\"\"Load a full NN classifier directly from a saved class instance.\"\"\"\n+    # WIP: Currently doesn't work.  Throws:\n+    # AttributeError: 'module' object has no attribute 'LogisticRegression'\n+    classifier = cPickle.load(open(filename))\n+    return classifier\n+\n+### RUN. ###\n+\n+# The routines below accept neural networks specified as instantiated\n+# instances of various DLN classes.  They return outputs for certain\n+# user-defined subsets of neurons.\n+\n+def fire_neurons(inputvecs,neurons,net):\n+    \"\"\"Given input vectors, compute the firing of specified neurons for NN\n+    net.\n+    \n+    :param inputvecs: numpy 2d array. Each row is an input vector with\n+    values given across columns.\n+\n+    :param neurons: list of neurons for which output is desired\n+\n+    :param net: class instance corresponding to a NN.  currently only\n+    single-layer logistic regression is supported.\n+    \n+    \"\"\"\n+    fire = theano.function(\n+        inputs=[],\n+        outputs=net.p_y_given_x,\n+        givens={net.input:inputvecs}\n+        )\n+    return fire()[:,neurons]\n+\n+def logreg_predict(inputvecs,classifier):\n+    \"\"\"Return the predicted output labels for input vectors.\"\"\"\n+    output_neurons = range(classifier.params[-1].get_value().shape[0])\n+    return numpy.argmax(fire_neurons(inputvecs,output_neurons,net),axis=1)\n+\n+\n+def logreg_validate(input,labels,classifier):\n+    \"\"\"Compute the fractional zero-one loss for the classifer on inputvecs,\n+    given labels.\"\"\"\n+    # WIP?  It seems to work but returns slightly different\n+    # numbers on MNIST than seen during training: On val set: .0743 vs\n+    # .075 during training; on test set: .0775 vs .0749 during training.\n+    loss = theano.function(\n+        inputs=[],\n+        outputs=classifier.errors(labels),\n+        givens={classifier.input:input}","path":"code/rebuild.py","position":153,"original_position":153,"commit_id":"c432c2ff1a9f7fe5af05c664ae1eae0e8c63aa4c","original_commit_id":"c432c2ff1a9f7fe5af05c664ae1eae0e8c63aa4c","user":{"login":"robinkraft","id":428784,"avatar_url":"https://avatars.githubusercontent.com/u/428784?v=3","gravatar_id":"","url":"https://api.github.com/users/robinkraft","html_url":"https://github.com/robinkraft","followers_url":"https://api.github.com/users/robinkraft/followers","following_url":"https://api.github.com/users/robinkraft/following{/other_user}","gists_url":"https://api.github.com/users/robinkraft/gists{/gist_id}","starred_url":"https://api.github.com/users/robinkraft/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/robinkraft/subscriptions","organizations_url":"https://api.github.com/users/robinkraft/orgs","repos_url":"https://api.github.com/users/robinkraft/repos","events_url":"https://api.github.com/users/robinkraft/events{/privacy}","received_events_url":"https://api.github.com/users/robinkraft/received_events","type":"User","site_admin":false},"body":"here 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