diff --git a/Project.toml b/Project.toml index fac4f07..049bc09 100644 --- a/Project.toml +++ b/Project.toml @@ -18,6 +18,7 @@ JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" MLBase = "f0e99cf1-93fa-52ec-9ecc-5026115318e0" MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458" MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54" +OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f" Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" TextAnalysis = "a2db99b7-8b79-58f8-94bf-bbc811eef33d" diff --git a/_toc.yml b/_toc.yml index 55aedc7..0f84a4a 100644 --- a/_toc.yml +++ b/_toc.yml @@ -39,5 +39,5 @@ parts: numbered: true chapters: - file: notebooks/chapter_recurrent_neural_networks/sequence.ipynb - - file: notebooks/chapter_recurrent_neural_networks/text-sequence.ipynbs + - file: notebooks/chapter_recurrent_neural_networks/text-sequence.ipynb \ No newline at end of file diff --git a/notebooks/chapter_recurrent_neural_networks/text-sequence.ipynb b/notebooks/chapter_recurrent_neural_networks/text-sequence.ipynb index ff14f1a..99bb2a1 100644 --- a/notebooks/chapter_recurrent_neural_networks/text-sequence.ipynb +++ b/notebooks/chapter_recurrent_neural_networks/text-sequence.ipynb @@ -20,7 +20,7 @@ { "data": { "text/plain": [ - "\"/tmp/jl_gxiB01gPaV\"" + "\"/tmp/jl_u5uDyJi9IX\"" ] }, "metadata": {}, @@ -336,22 +336,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Partitioning Sequences\n", + "## Recurrent Neural Networks with Hidden States\n", "\n", - "To train language models, we will randomly sample pairs of input sequences and target sequences in minibatches. The following data loader randomly generates a minibatch from the dataset each time. The argument batch_size specifies the number of subsequence examples in each minibatch and num_steps is the subsequence length in tokens." + "To begin with, we define matrices X, W_xh, H, and W_hh, whose shapes are (3, 1), (1, 4), (3, 4), and (4, 4), respectively. Multiplying X by W_xh, and H by W_hh, and then adding these two products, we obtain a matrix of shape (3, 4)." ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "86709-element DataLoader(::Tuple{Matrix{Int64}, Matrix{Int64}}, batchsize=2)\n", - " with first element:\n", - " (10×2 Matrix{Int64}, 10×2 Matrix{Int64},)" + "3×4 Matrix{Float64}:\n", + " -1.98493 2.83654 0.591937 -0.426544\n", + " -2.20367 7.39051 -2.28091 -1.21806\n", + " 1.11862 1.13828 -2.7502 0.286596" ] }, "metadata": {}, @@ -359,70 +360,63 @@ } ], "source": [ - "using IterTools\n", - "using Flux\n", - "\n", - "num_steps=10\n", - "batch_size=2\n", - "function time_machine_dataloader(num_steps::Int,batch_size::Int)::Flux.DataLoader\n", - " partitions = hcat([to_indices([i...]) for i in [i for i in partition(str,num_steps,1)]]...)\n", - " x = partitions[:,begin:end-1]\n", - " y = partitions[:,2:end]\n", - " return Flux.DataLoader((x,y),batchsize = batch_size)\n", - "end\n", - "\n", - "train_loader = time_machine_dataloader(num_steps,batch_size)" + "X, W_xh = randn(3, 1), randn(1, 4)\n", + "H, W_hh = randn(3, 4), randn(4, 4)\n", + "X*W_xh+H*W_hh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see in the following, a minibatch of target sequences can be obtained by shifting the input sequences by one token." + "Now we concatenate the matrices X and H along columns (axis 1), and the matrices W_xh and W_hh along rows (axis 0). These two concatenations result in matrices of shape (3, 5) and of shape (5, 4), respectively. Multiplying these two concatenated matrices, we obtain the same output matrix of shape (3, 4) as above." ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 14, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 2; 2 3; 3 4; 4 1; 1 5; 5 6; 6 3; 3 4; 4 6; 6 7][2 3; 3 4; 4 1; 1 5; 5 6; 6 3; 3 4; 4 6; 6 7; 7 8]" - ] + "data": { + "text/plain": [ + "3×4 Matrix{Float64}:\n", + " -1.98493 2.83654 0.591937 -0.426544\n", + " -2.20367 7.39051 -2.28091 -1.21806\n", + " 1.11862 1.13828 -2.7502 0.286596" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "for (x,y) in train_loader\n", - " print(x)\n", - " print(y)\n", - " break\n", - "end" + "cat(X,H; dims = 2)*cat(W_xh,W_hh;dims = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Recurrent Neural Networks with Hidden States\n", + "## One-Hot Encoding\n", "\n", - "To begin with, we define matrices X, W_xh, H, and W_hh, whose shapes are (3, 1), (1, 4), (3, 4), and (4, 4), respectively. Multiplying X by W_xh, and H by W_hh, and then adding these two products, we obtain a matrix of shape (3, 4)." + "For example, if the vocabulary had five elements, then the one-hot vectors corresponding to indices 1 and 3 would be the following." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3×4 Matrix{Float64}:\n", - " 0.241561 0.480103 0.83227 -2.63924\n", - " 1.90312 0.545007 -0.470233 1.15789\n", - " -0.653043 -0.534098 3.13261 -0.812047" + "5×2 OneHotMatrix(::Vector{UInt32}) with eltype Bool:\n", + " 1 ⋅\n", + " ⋅ ⋅\n", + " ⋅ 1\n", + " ⋅ ⋅\n", + " ⋅ ⋅" ] }, "metadata": {}, @@ -430,38 +424,54 @@ } ], "source": [ - "X, W_xh = randn(3, 1), randn(1, 4)\n", - "H, W_hh = randn(3, 4), randn(4, 4)\n", - "X*W_xh+H*W_hh" + "using OneHotArrays\n", + "\n", + "onehotbatch([1,3],1:5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we concatenate the matrices X and H along columns (axis 1), and the matrices W_xh and W_hh along rows (axis 0). These two concatenations result in matrices of shape (3, 5) and of shape (5, 4), respectively. Multiplying these two concatenated matrices, we obtain the same output matrix of shape (3, 4) as above." + "（修改）\n", + "The minibatches that we sample at each iteration will take the shape (batch size, number of time steps). Once representing each input as a one-hot vector, we can think of each minibatch as a three-dimensional tensor, where the length along the third axis is given by the vocabulary size (len(vocab)). We often transpose the input so that we will obtain an output of shape (number of time steps, batch size, vocabulary size). This will allow us to loop more conveniently through the outermost dimension for updating hidden states of a minibatch, time step by time step." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 60, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "3×4 Matrix{Float64}:\n", - " 0.241561 0.480103 0.83227 -2.63924\n", - " 1.90312 0.545007 -0.470233 1.15789\n", - " -0.653043 -0.534098 3.13261 -0.812047" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "size(([model(xs[i]) for i = 1:10])[1]) = (27, 2)\n" + ] } ], "source": [ - "cat(X,H; dims = 2)*cat(W_xh,W_hh;dims = 1)" + "model = Chain(RNN(27 => 32),Dense(32=>27))\n", + "\n", + "num_steps = 10\n", + "\n", + "a = [onehotbatch(to_indices(collect(i)),1:27) for i in partition(str,num_steps,1)]\n", + "x = a[begin:end-1]\n", + "y = a[2:end]\n", + "\n", + "loader = Flux.DataLoader((x,y),batchsize = 2)\n", + "\n", + "for (x,y) in loader\n", + " xs = eachslice(cat(x...;dims=3);dims=2)\n", + " ys = eachslice(cat(y...;dims=3);dims=2)\n", + " [model(xs[i]) for i in 1:10 ]\n", + " break\n", + "end\n", + "\n", + "# 时间步长度x字母特征\n", + "# x = partitions[:,1:5]\n", + "# # sequence_length x batch_size\n", + "# x\n" ] }, { @@ -475,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -509,22 +519,40 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "size(x) = (32, 1024)\n", - "model(x) = Float32[-0.57374346 -0.26441568 -1.9320982 -2.848492 -2.1397445 -2.4540977 -1.7840853 -1.648342 -2.52589 -1.8467429 -1.2987548 -2.2716665 -2.1326025 -2.3978176 -0.98061323 -1.9477181 -1.280873 -0.99040264 -1.2784705 -1.547663 -1.4533584 -1.1009576 -0.8664077 -0.765324 -0.90174484 -1.0487828 -1.0890119 -1.524941 -1.7626832 -1.5131028 -2.7616706 -2.0652514 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1.3902465 1.4880302 1.8306949 -0.08938725 0.7861296 1.485556 0.9159959 0.39588434 1.3020797 0.37178776 1.3105526 1.990368 1.060736 0.88953626 1.0233319 1.8264737 1.1088042 1.0274426 1.3595434 0.5563574 0.6133702 1.843389 1.0262158 1.4318419 2.5846214 1.256445 2.7250068 1" + "size(x) = (10, 2)\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Excessive output truncated after 574663 bytes." + "ename": "DimensionMismatch", + "evalue": "DimensionMismatch: layer RNNCell(32 => 32, tanh) expects size(input, 1) == 32, but got 10×2 Matrix{Int64}", + "output_type": "error", + "traceback": [ + "DimensionMismatch: layer RNNCell(32 => 32, tanh) expects size(input, 1) == 32, but got 10×2 Matrix{Int64}\n", + "\n", + "Stacktrace:\n", + " [1] _size_check(layer::Flux.RNNCell{typeof(tanh), Matrix{Float32}, Matrix{Float32}, Vector{Float32}, Matrix{Float32}}, x::Matrix{Int64}, ::Pair{Int64, Int64})\n", + " @ Flux ~/.julia/packages/Flux/Wz6D4/src/layers/basic.jl:195\n", + " [2] (::Flux.RNNCell{typeof(tanh), Matrix{Float32}, Matrix{Float32}, Vector{Float32}, Matrix{Float32}})(h::Matrix{Float32}, x::Matrix{Int64})\n", + " @ Flux ~/.julia/packages/Flux/Wz6D4/src/layers/recurrent.jl:204\n", + " [3] Recur\n", + " @ ~/.julia/packages/Flux/Wz6D4/src/layers/recurrent.jl:134 [inlined]\n", + " [4] macro expansion\n", + " @ ~/.julia/packages/Flux/Wz6D4/src/layers/basic.jl:53 [inlined]\n", + " [5] _applychain\n", + " @ ~/.julia/packages/Flux/Wz6D4/src/layers/basic.jl:53 [inlined]\n", + " [6] (::Chain{Tuple{Flux.Recur{Flux.RNNCell{typeof(tanh), Matrix{Float32}, Matrix{Float32}, Vector{Float32}, Matrix{Float32}}, Matrix{Float32}}, Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}})(x::Matrix{Int64})\n", + " @ Flux ~/.julia/packages/Flux/Wz6D4/src/layers/basic.jl:51\n", + " [7] macro expansion\n", + " @ ./show.jl:1181 [inlined]\n", + " [8] top-level scope\n", + " @ ~/github/D2lJulia/notebooks/chapter_recurrent_neural_networks/text-sequence.ipynb:4" ] } ], @@ -534,7 +562,10 @@ " @show size(x)\n", " @show model(x)\n", " break\n", - "end\n" + "end\n", + "# batchsize * ( feature * timestep) \n", + "# =>\n", + "# timestep * ( feature * batchsize)" ] } ],